Mining Drug Space

Top 10 misconceptions in science communication

2012-01-02T14:35:00.014+01:00

I would like to trigger some warnings about typical misconceptions in personal science communication. I have read the blog posts of Tony and Graham about social networking tools for scientists, and got indeed left with the feeling that it is all about tools and the right databases. To be clear, it is not! I have, of course, also read the books about Collaborative Computational Technologies for Biomedical Research and Reinventing Discovery. One thing is very clear, it is not about information (tools/data) alone, it is not about people alone, but Knowledge=People+Information:

People learn in response to need. When people cannot see the need for what's being taught, they ignore it, reject it, or fail to assimilate it in any meaningful way. [Social media needs to support different scientific personalities]

I can understand that many scientists might not have a lot of experience with PIM or KM (Personal Information Management or Knowledge Management), since it sounds unsexy for many researchers and research grants. Still, maybe some science-sustainability thinking would be good, otherwise we are facing more data tombs, either in science information, science services, or personal expert profiles.

Now, here the top 10 of misconceptions in science communication:

1. People know how to find me.
Then you must have a very unique name and are assuming that all others are already knowledge ninjas. In reality have most other people troubles finding, re-finding information (credits:KFTF) or people (credits:Cameron Neylon).

2. People make extra efforts in finding me or my research results.
Forget it! Most of us are drowing in information and workplace/private distractions. Most of us are already exhausted. So, if you want that others read more information about yourself or your research results, make it easy for them getting, finding, and re-finding it. Yes, this might require some learning from you on how to make it easier for others. For making the cognitive surplus possible everyone needs to understand the limitations of human potential and how to change behavior and current practice. I fully agree with Shirky's vision on this.

3. My university profile page is persistent and a good expert profile maintenance scenario.
Then why are asking publishers for your email and institute and not for your profile page? There is a reason the science community is interested in ORCID (Open Researcher and Contribution ID)! Please check some of Martin Fenner's slides to learn more.

ORCID aims to solve the author/contributor name ambiguity problem in scholarly communications by creating a central registry of unique identifiers for individual researchers and an open and transparent linking mechanism between ORCID and other current author ID schemes. [ORCID.org]

Besides, how stable is a university group profile when changing groups? Yes, there are more persistent expert profile and expert activity solutions out there, use them.

4. I am a scientist and journal publications are sufficiently granular as communication form.
Oh really? We know that people loose interest in scientific publications rather quickly. Why should your publications be the exception? Get over it and be realistic, it is not about you, it is about the crowd, so go where the crowd is, and question/answer systems are clearly one possibility for sharing your expert knowledge in more incremental ways. There is no 'one size fits all', if I have a simple question, do I really need to read all your publications? Think about it...

5. Social media technology will fix all your problems.
Nope, at the end of the day it is about people collaborating with another. Technology just serves as amplifier.

If you do not enter the groundswell with a clear goal you will fail. [Groundswell]

So, what is your goal?

6. Sharing information is good enough.
People want knowledge, but for getting it they have to make sense of information and learn. So, just sharing information requires to spend time for making sense out of it and taking it in. There is a reason for caring about effective presentations (MP3, credit: Jean-Luc Doumont). Yes, the web is one way to present things to others. Please make an effort structuring your thoughts and I can also recommend the following book offering some help: Trees, maps, and theorems - Effective communication for rational minds.

7. Sharing raw data is good enough.
If information sharing (point 6.) requires efforts from others to take it in, do you then really think sharing raw data will require less efforts? In the contrary, it takes even more efforts, and please be aware that people are different, so are their information management behaviours. Right, it is not about you, alone, it is about them!

8. If I become or make 'Open XYZ' everybody will come to me.
Simply, no! I clearly agree that open research, communication, source, and data helps in extending collaboration networks, but just making things 'open' is not a sufficient strategy.

9. Hope (for altruism) is a strategy.
No, and especially in science the crowds are becoming more specialized and smaller. Do you remember that we had such discussions before?

What are the measures that can make community genome annotation a success?

Without measuring and caring for contributions of others and their peers, you will fail. You have your own site and data, fine, and can others integrate it into their networks? Think about their metrics, yours is secondary!

10. There is a free lunch, and everything I do is already supporting others in their needs.
Then you are clearly able to handle that people have more and more choice and run with what is best for them, congratulations! You know that free means that free!

More reading
Much more insights can be found din various related books, check my reading list. As a shortcut, I am always happy to talk, present, or collaborate with you.

Should scientists care about a Wiki for Knowledge Management?

2011-03-06T19:08:00.014+01:00

I would like to encourage scientists to contribute to the ongoing survey of the Research Committee of the Wikimedia Foundation. The title of the survey is "Expert barriers to Wikipedia?" and you as scientists, experts, would greatly help in understanding what drives people to contribute, collaborate, and communicate with each other. The focus of their survey is on Wikipedia as Knowledge Management (KM) platform.

I took the survey and add here additional remarks to some science specific issues:

We have featured articles on Wikipedia. Do we also have "expert approved" articles? If not, could the WM:ResearchCommittee create some guidelines, and expert networks in taking this post-review on? This would help having a better argument against those typical "Is WP a reliable source?" discussions, especially if it comes to "expert topics", e.g. medical or scientific topics.
Journal articles and conference articles can be cited and have unique Digital Object Identifiers. Can each WP article ensure getting a proper citable DOI?
What is the impact factor for WP? If it only applies to journals, then what would be required for enabling a WP:Journal? Besides, if there is no number, there is no incentive! Many people typically say this pointing to abusing free-riders and Altruism should not be overrated, since conditional collaborators (the largest crowd) will only contribute when they do not feel abused by free-riders. Besides, expert recognition from other experts is critical, too.
Edit counts on WP are easy to calculate, but how do we compare expert contributions from correcting spelling errors? Do not get me wrong, every contribution counts, I agree with Shirky that " the smallest creative act, is still a creative act". Anyway, some experts care a lot for being recognized as an expert contributor, if this is not the case, they might turn from being "conditional collaborators" to "non-collaborative" WP users.

Finally, Let us never forget that Knowledge=People+Information, so WP:Wiki technology alone will not move anywhere, if people cannot see the need for it, or do not get the kudos they deserve in a proper peer-recognition manner. If people get the feeling that there is no audience or not the right audience caring for contributions, people simply stop contributing.

The following two slide decks fit into this discussion of a "Wikipedia-Myth", and some might prefer looking at them for stimulating thoughts about Wikipedia as KM platform.

Let us know what you think by contributing to the WM:Survey or comment below and we will forward your thoughts to the WM:ResearchCommittee.

Thank you!

The Wikipedia Myth - Enterprise 2.0 Knowledge Management

Enterprise 2.0 Knowledge Management - People at the Center

Did you already take the survey? Just checking ... ;-)

Life Science in 2020 - Vision of students joining ESOF 2010

2011-01-09T17:28:00.007+01:00

Here more information about the Janssen ScienceBus 2010 to the ESOF conference (EuroScience Open Forum).

Life Science in 2020 - Vision of the students
Booklet from Alfons Boesmans

See also

It's not filter failure. It's a discovery deficit.

2010-11-11T12:01:00.004+01:00

Great Lessig styl'ish presentation @Cameron.

"Don’t think about filtering.
Don’t think about control.
Enable discovery."

I simply agree, and we need more "need alignment" discussions and sufficient resources for making this possible! Aka, I consider a discovery deficit as a lack of enabling true network building and network maintenance scenarios.

It's not filter failure. It's a discovery deficit.

View more presentations from Cameron Neylon.

For related posts see also

FOAF - a decentralized solution for knowledge=people+information
Social media and science personalities, let us align our needs and ensure building relationships
Innovation 2.0, especially in drug design
Information overload is a challenge we have to work on in science, e.g. via Enterprise 2.0 strategies
Only, if we work on all of those things we can enable data-driven and six-sigma-driven drug design

Science plog'ing, S3'ing, and other trends

2010-09-01T20:13:00.009+02:00

After the Pepsi scandal in the scientific community it is nice to see that the scientific bloggers are moving on and yes

"If science bloggers (and blogging networks) work together, we can reach for the stars" [@mfenner]

Two recently launched blogging networks are

Science 3.0
PLoS blogs

Molecular design and molecular modeling basics

2010-05-08T21:21:00.006+02:00

After a recent question on Blue Obelisk Exchange and ChemPedia Lab about molecular drug design (see also cross-post) and a recent book about molecular modeling basics I thought it is time highlighting more information on that topic.

Drug design principles, molecular modeling, and use in medicinal chemistry:

C. Bissantz, B. Kuhn, and M. Stahl, A Medicinal Chemist’s Guide to Molecular Interactions, J. Med. Chem., 2010, Article ASAP. DOI 10.1021/jm100112j
Bioisosterism and bioisosteric design principle articles.
J. H. Jensen, Molecular modeling basics, 2010.

Summary slides about molecular modeling (quantum chemistry) of Noel O'Boyle:

Friend of a friend - How to create a FOAF science network?

2010-02-08T12:20:00.013+01:00

I strongly believe in

Knowledge = People + Information

Thus one critical key aspect is allowing me and my friends (people) finding each other and browsing the information from each other easily. One of the most person-centric ways of defining people-networks are friend-of-a-friend (FOAF) networks. Here is why:

First, this is independent of any social networking provider, which might not allow you tracking and entering all the information you need into their system.
Second, there are multiple ways how you can create FOAF ontologies, e.g. identi.ca, Quatuoa, and Pierre's XSL transformation from a LinkedIn profile. I used the following starting points
- FOAF examples on foaf-visualizer.org
- Description Of A Career (DOAC) entries from my LinkedIn profile using Pierre's XSL
- Finally, I placed my FOAF profile on GIT, please feel free using this as starting point for your FOAF profile.
Third, put your FOAF profile on any web-server and enable autodetection from your sites, and your semantic people network is ready! Note that Google is allowing you to import FOAF ontologies into Google profiles. Since this information can be used for searching within your social circle, this clearly facilitates finding each other and finding information within social networks.
Fourth, there are multiple visualization and browsing options for FOAF networks, e.g. FOAF visualizer, FOAF browser.

I enabled those visualizations on my blog and homepage using the logos below, please feel free to check the output when clicking on them:

FOAF visualizer

FOAF browser

Update after a discussion with @melvincarvalho and @yokofakun:

Make sure adding a foaf:PersonalProfileDocument to your profile. Again, see my example.
Please make sure validating your FOAF profile with the W3C RDF validation service.
There is an option adding research papers using the XSL of @yokofakun (I have still not done, yet)
Finally, here are a few more visualization options:

Social media needs to support different scientific personalities

2009-12-20T13:21:00.007+01:00

Derek started an interesting discussion about social media in chemistry based on a nature commentary.

Here my comment
Please let us not forget that there are different 'scientific personalities' [1] as there exist different 'information management personalities' [2]. I think we should appreciate the diversity and never even try to force all scientists behaving the same. Nonetheless, would I like to see more scientists contributing to social media. Especially supporting various personalities for various reasons in multiple ways !

Now, if scientists do not contribute to social media then for me the question is rather what are we doing wrong in supporting their needs for learning/contributing [3]? Chemistry always was and still is a very (article) reading intense area and I am still wondering if 'information overload' of yet-another-information-channel might be one cause of social media inactivity for the 'older scientist generation'. The youngsters (generation X and Y) are clearly using social media, but might not have the highest scientific profiles, yet, but some of them will have it soon!

I think the following quote is very true, what do you think?

"People learn in response to need. When people cannot see the need for what's being taught, they ignore it, reject it, or fail to assimilate it in any meaningful way." [4]

References
[1] http://bit.ly/5teuwO
[2] http://bit.ly/87qcST
[3] http://bit.ly/XAvjK
[4] http://bit.ly/51I8k1

Sky-rocking graphics in new MOE release

2009-12-13T14:57:00.008+01:00

Have you seen the new graphics functionality in the new MOE (Molecular Operating Environment) of the Chemical Computing Group (CCG)? No? You need to look at some examples in their image gallery! The graphics engine is by-far the best I have seen since a long time, even without using 3D shutter glasses.
Besides, the release has not only an improved look-and-feel, but also (as usual) a lot of high-level algorithm improvements. Here an overview about the new features of the 2009.10 release:

Real Time Ray-traced Graphics
Protein/Antibody Modeling
LowModeMD Conformational Search
Synthetic Score Descriptor
Scaffold Replacement/Fragment Linking
MOE/web SOAP Server

Finally, for those being interested in using Schrodinger's or CCG's tools in combination with KNIME, there is good news. After a long period of pushing, the KNIME crew released a reporting collection and enterprise products, e.g. server and cluster executions. This is definitely a step towards business intelligence and certainly an important achievement of the development and strategic planning team.

Warren DeLano - In Memoriam

2009-11-07T13:34:00.007+01:00

The family of Warren Lyford DeLano has created a "In Memorium" page and blog. Please share your memories there !

As many others, I am shocked, sad, and are left with the impression having lost something important. We never met in real-life, but exchanged thoughts in several forums, eMails, and other new generation media. He was a natural leader in the life science informatics community (PyMol, open source) and paved a way for many of us. He will never be forgotten !

Twitter reactions

Blog reactions

Innovation 2.0 - Looking for a strategy in drug design and health care?

2009-09-14T20:21:00.006+02:00

"You can’t control what you can't measure." [Tom DeMarco]

I am strongly believing in people-centric innovation for drug design and health care. Still, we have serious challenges to solve before this can happen, and a lot of them are in-vivo, not in-silico:

Get rid of the negative group dynamics.
Accept information overload and embrace collaboration.
Bridge silos, e.g. chemical data silos, but also break-down legal or license hurdles, e.g. by using micropayments.

Please find below the slides and handout for my talk in October on the BioIT World conference. The conference will cover a large range of life science topics, e.g. -omics, semantics, technology, bioinformatics, life science software, collaboration platform, etc. This is all very exciting and I am looking forward to those talks and meeting people in real life.

Please let me know, if you agree with the challenges, and what you have experienced for using collaboration solutions in a commercial setting. I am a technological geek (tekki), and here I am especially interested in the opinions of the non-tekkis !

Talk to you soon ...

Drug Innovation 2.0 (Slides)

View more presentations from Joerg Kurt Wegner.

Drug Innovation 2 (Handout)

View more documents from Joerg Kurt Wegner.

See also

What is an innovation funnel ?
Is drug design really rational ?

50 million chemicals, and accelerating - chemical patent overload?

2009-09-10T21:07:00.006+02:00

(via NatureNews) As reported by the Chemical Abstract Service (CAS) have they reached 50 million registered chemical entities.

Here some numbers to think about

CAS needed 33 years for registering the first 10 million compounds, that is one new compound every 101.5 seconds.
the last 9 months CAS registered 10 million compounds, in other words, every 2.3 seconds a new compound was created !
So, chemistry has improved its efficiency 44 times !

This resulted in a drastically increasing patenting rate as mentioned by David Bradley.

O.k. what happens now to your actual lead structure series?

Don't panic! Just get enough money for mining CAS directly ... mmmh ... just kidding.
Second choice? Extract all 50 million structures from PDF patents, which is really difficult and noisy, I mean it.
Third choice? Just get back to your binding energy modelling, solvation modelling, and PK modelling and assume the structures are patentable. Under the bounded rational drug design paradigm is this statistically a reasonable approximation. But wait ! In contrast to statistics (where the average counts) is one patent enough to kill your lead series. ... o.k. ... now you can panic !

See also

The Web for Patients, Pets, and Scientists (Panel Discussion)

2009-08-15T19:49:00.008+02:00

(via Jean-Claude Bradley)

The panel discussion from Jimmy Wales (founder of Wikipedia), John Wilbanks (VP Creative Commons), and Stephen Friend (founder of sage) highlights a few examples of the web for scientists, pets, and patients.

Army of women for breast cancer trials (driven by Susan Love)
Post-clinical trials, or patients sharing treatment details with each other - PatientsLikeMe
PetDiabetes Wiki

John Wilbanks made some really nice statements reflecting a deep understanding in the web and scientific world out there

Actually, the scientific process is not so different from Wikis, in one you publish paper-by-paper, in Wikis you publish edit-by-edit. Though it might be a little slow and top-down.
In life sciences we have to be aware that we are talking about an evolved system (life), not a designed system (a chip), so never underestimate the role of human compilers processing information, in contrast to machine compilers processing information.
Comment JKW: I pretty much agree on this, and disagree with Stephen Friend, which mentioned that experts are overestimated.

Finally, critical problems to solve are

standardization, e.g. gene identifiers, chemical identifiers
stabilizing web-services, e.g. avoid change URLs, or provide redirects or schema backward compatibility
ensure stability and maintenance of services beyond funding periods
improve understanding of ambiguities, e.g. create semantics and ontologies

Making the Web Work for Science - Full from Jordan Mendelson on Vimeo.

Google Wave - Mission Drug Design Federation

2009-06-07T19:02:00.017+02:00

"The Google Wave Federation Protocol may succeed email (an innovation from 1965), as the dominant form of Internet communication."
[Google Wave Federation Protocol @WP]

Google launched Google Wave. It is a mind-blowing technical platform, which might just "change" publishing, health support of patients and physicians, and any other industry, where knowledge workers are suffering from keeping found things found (KFTF) and collaboration security (see conference blogging, libel law, compliant commenting, medicine 2.0 danger). I do not think the GWave will replace collaboration tools, but it might enrich them, by facilitating information moderation and bridging (for all peers and channels).

You can check first the Google Wave presentation (almost 1.5 hours), or continue reading below.

What is the negative side?

There are two critical posts about GWave (1,2), and I comment on them here.

like instant messaging, an attention keeper. I agree, but you could say the same for phone calls, meetings, or any other some2some, or some2many communication form. So, this can be easily disarmed by providing an "I am in the work-flow or busy" button, so do not expect immediate response. Just leave me alone and do not cause a workplace distraction.
instant typing and sentence rewriting is creepy. I agree, it is like sitting in front of a washing machine, not really educational. There should be some degree of federation cache a user can trigger. Sometimes I might want to write even a full paragraph before getting viewed by others.
CCing wrong person. This is a security leakage problem, and can happen with any electronic information. It is just a gut feeling, but I would assume that GWave could even trigger a recall or hold-back at any later stage. So, it might be even safer then the actual eMail system? Anyway, I still think that all confidential data should come with a peer2peer privacy option, or any other form of encryption/decryption.
another silo of communication. A what? If anything can bridge silos, then communication. The demo has shown that GWave can integrate already many other data silos, and it provides even an open communication layer. Thumbs up for Google for making the standard open.
user-wall gardening. This is serious and opens again the question how many IDs a researcher can and should have nowadays. Why do you think are people talking about global IDs, like OpenID? Exactly, to get rid of all the many single logins. So, I rather trust Google then any other small startup for hosting my identity information. Though, any company with a lot of data has to take user data, especially patient data, serious, right last.fm?
eMail threads work, so why the Wave hype. Honestly, I had already many2many threads with overlapping replies, and believe me, eMail fragmentation is a problem. At some point I was ending up with copying eMail parts to a word processor for allowing me to follow the argumentation sub-trees. And no, a live meeting is not an option, if people are in different (overlapping) meetings, buildings, and countries.

What is in there for drug design?

The key feature is the federation of data and people sources, something like FriendFeed behind your company firewall in a secure legal environment.

"@Google, please take data security very seriously, only a few people might want that confidential data is moving to your server. We can talk about contribution tokens and embedding tools, but data should stay in the remote company silo." [personal note]

Business Intelligence and News Alerts. You might follow a series of business intelligence news, journal article news, or other news feeds. Is it not sometimes a pity, that the commenting happens all over the place? Sure, there are BackType and Disqus for a comment federation, but it would be so much nicer, if everything could be federated in one stream. The Google Wave demo showed a federation example with a blog post, so I assume this will be possible.
Dynamic Reporting. Imagine you get an updated data report for your project X. As usual, people can reply via eMail and tell you what is nice or unexpected. But, it would be so much nicer, if people could actually collaborate on the data in one workflow, instead of switching forward and backward between different data sources and communication media. This could really boost efficiency.
Data Silo Bridging. Like many other tools is the trend going towards peer and feed federation, like on FriendFeed, Yahoo pipes, or RSS readers. This are all tools with their own API. In other words, you will face the same communication problems you have with data warehouses, either you create all NxN relationships between the services, or communication will be inefficient. Why do you think have people invented BioDAS or BioMART? Exactly, for having only one service many others can talk to, just like Google Wave. This will reduce the number of communication and API bridging efforts dramatically.
Project Feedback. If I have a question within a project where the project members are not sitting in my room, my building, my campus, or even my country. What can I do? I write an eMail, and expect sometimes tree-like eMails threads, which are overlapping. If you like playing eMail puzzle, this might be fun, it is certainly not when you are having a busy work day. Then eMail fragmentation is a distraction. GWave looks promising and might just avoid this problem, totally.

Finally, I think Google Wave can certainly reduce the information fragmentation and data silo bridging problem with its new and open federation protocol.
I hope it might find its way into businesses with strict communication regulations, eg. Pharma, Medicine, or any other organization dealing with patient data. I further hope that Google takes data security and data duplication issues very seriously in their federation protocol. Google might have here an ability to tackle both things, I am looking forward to it.

Drug popularity (via Google queries) - Yet Another Long Tail (YALT)

2009-05-30T00:09:00.010+02:00

Here some drug popularity trends based on automatic Google queries. The drug names were taken from DrugBank.

The top ten are

Cholesterol
Aspirin
Ethanol
Ibuprofen
Insulin
Acetaminophen
Testosterone
Cocaine
Oxycodone
Vardenafil

References

I used a Python script for the Google data fetching. Let me know, if you need more details.
The Long Tail @Wikipedia
DrugBank: a knowledgebase for drugs, drug actions and drug targets.
Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M.
Nucleic Acids Res. 2008, 36, (Database issue):D901-6.
PMID: 18048412
DrugBank: a comprehensive resource for in silico drug discovery and exploration.
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J.
Nucleic Acids Res. 2006, 34, (Database issue):D668-72.
PMID: 16381955

Compliant commenting in science 2.0

2009-05-26T22:18:00.008+02:00

We know that scientific collaboration is important, but what is holding people back to contribute to web 2.0 communities (of people, like you!) ?
This question was raised on FriendFeed (another web 2.0 platform, a good one).

Though I answered parts of this question earlier, here a focus on the increasing number of compliance regulations, especially for people in industry (e.g. drug design). Beside, please check also the dangers in health/medicine 2.0.

Science And Compliant Commenting

View more OpenOffice presentations from Joerg kurt Wegner.

Peer recognition and building relationships - Even in drug design

2009-05-19T21:16:00.012+02:00

"Experts will want to contribute to coworkers who need them, who will hear them, who will respect them and who may even thank them. ... Rather than asking, How do we incentivise people to share their knowledge? It would be more useful to ask, How do we develop relationships across the globe that will set in motion more knowledge sharing?" [slightly generalized after Nancy Dixon]

"Today there seems to be a new distribution model that is emerging. One that is based on people’s ability to publically syndicate and distribute messages — aka content — in an open manner. This has been a part of the internet since day one — yet now its emerging in a different form — it's not pages, it's streams, its social and so its syndication." [John Borthwick]

Recently, there are many efforts ongoing to build relationships in the drug design community, and yes, the peers are recognizing and appreciating those efforts.

WikiChemists and Chemical Abstract Service (CAS) announced their collaboration. Beside, we should never forget that this is a community achievement of many people, especially the WikiChemists and everybody involved in ChemSpider (appreciated by RSC). Dear CAS, we appreciate, that you appreciate.
ChemMantis (slides) and Reflect (webcast) bridge chemical and biological data silos. This saves time and enriches information and is peer appreciated.
Biskit bridges tools and services manipulating and analyzing macromolecular structures, protein complexes, and molecular dynamics trajectories. If you ask me, any efforts making daily life easier and giving access to many tools via one interface is peer appreciated.
BioMart and DAS (Distributed Annotation System) are both systems designed for accessing many biological data sources via a unified interface. This is peer appreciated.
The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. The community is using it and contributes to the development of convergent data standards in omics research. Clearly, peer appreciated.
When thinking about drug or side-effect re-use, a lot of information and people were involved in creating this information. So, helping patients by bringing drugs to people to drugs to people to ... is peer appreciated.
Predicting novel targets for existing drugs using side effect information

Drug design and thinking the unthinkable

2009-05-06T20:32:00.015+02:00

"The problem pharma industry faces isn’t that they didn’t see generics coming. They not only saw it miles off, they figured out early on that they needed a plan to deal with it. ... In drug design the unthinkable scenario unfolded something like this: The ability to share data and intellectual properties wouldn’t shrink, it would grow. Walled data silos would prove unpopular and inefficient. ... No one experiment is going to replace what we are now losing with designed drugs, but over time, the collection of new experiments that do work might give us the cure we need." [adapted from Clay Shirky]

We need legal support, intellectual property protection, and social legal systems, we need them more than ever ! Sure, we want to work 'openly' together, but not without payment. I think there are little intentions of patients and scientists to starve themselves to death for providing a health benefit to others. The key question is therefore how we can measure knowledge and contributions allowing us to reward the support of patients and scientists? Is there any metric or legal system we can adapt? If you have any idea, let me know !

"You can’t control what you can't measure." [Tom DeMarco]

What can we learn from

Lawrence Lessing (legal aspects of free cultures),
Andrew McAfee (Enterprise 2.0), and the
Do Tank for delivering Do Drug Development (D³) solutions

Open innovation in drug design? - I do not think so!

2009-03-30T19:30:00.017+02:00

Triggered by slides and a discussion from Cameron Neylon was I wondering, to which degree people believe in open innovation for the drug design community?

Though the principle seems valid for a few software companies, is there little support for life sciences on this topic.

"It's an area that's just not been explored much. The usual flow is very much linear. Can be developed in academia, is then licensed via tech transfer." [Deepak]

"The question is whether a business can make money by a route that doesn't involve patents (or copyright). There are examples of that, O'Reilly publishing being the most obvious but they are pretty thin." [Cameron]

In other words, if you do not need the money, do whatever you like openly.

If you need the money, e.g. for clinical trials, then please make sure staying within the same legal framework, which is most probably not open! Do not misunderstand this, I still strongly believe in (the legally encapsulated) web 2.0 !

Drug design is not like the IT industry

Some people think drug design is like chip or software design, e.g. by (over)simplifying the business models. I heard that before, and I still doubt so! Chip and software design are mostly an engineering task of known facts or an optimization of measurable parameters. In contrast, drug design uses often facts or parameters from the tip of the iceberg, but there are much more unknowns , or even things we can not look at (like *all* metabolic pathways in humans)! Beside, the exploding amount of information is making things not easier.

On the other hand, who could blame humans for oversimplifying problems ...

Unless we concentrate very hard, we often simplify a problem, because our minds routinely do so without knowing it.
Many people confuse the statement that "almost all drugs are small molecules" with "almost all small molecules are drugs". Assume that the first statement is true, that 96 percent of drugs are small molecules (not counting biotech, or nutraceutical drugs). This would mean that only about 0.01 percent of small molecules are drugs, since there are more than 24 million physically existing small molecules and only 4600 drugs, roughly one in five thousand. So the logical mistake makes you (unconsciously) overestimate the odds of a randomly drawn small molecule being a drug by more than five thousand times ! [freely adapted after The Black Swan, p 52]

References

4637 small molecule, 137 biotech, and nutraceutical drugs as taken from DrugBank 2009-03. The number of 24 millions physically existing (as published, not as existing or purchaseable) small molecules was taken from a recent CAS publication.

The future of science and scientists - walking into open innovation ?

2009-03-26T21:23:00.011+01:00

"If you just build it they (probably) won’t come...
If you don’t build it they deﬁnitely won’t come...
The community is more important than the service...
Researchers are already the tail !" [Cameron Neylon]

"An ideal collaboration market will enable just such an exchange of questions and ideas. It will bake in metrics of contribution so participants can demonstrate the impact their work is having. Contributions will be archived, timestamped, and signed, so it’s clear who said what, and when. Combined with high quality filtering and search tools, the result will be an open culture of trust which gives scientists a real incentive to outsource problems, and contribute in areas where they have a great comparative advantage. This will change science." [Michael Nielsen]

"Specialization will be one of many factors influencing creation of new types of scientists. And what are these types? Let me describe a few." [Pawel Szczesny]

Walking the walk - the practical experience of Web2 in research

View more presentations from CameronNeylon.

Etech Wilbanks via common knowledge, Lions, Tigers, Crowds

View more presentations from John Wilbanks.

Bridging chemistry islands to the web data silo

2009-03-22T16:04:00.008+01:00

Triggered by a few announcements of major publishers and web-services I could not resist to create a little chemical-web 2.0-story.

Bridging Chemistry Islands

View more presentations from Joerg kurt Wegner.

Do drug development (D3) - Innovation while information ignorance is increasing?

2009-03-18T21:27:00.011+01:00

People thought always drug design is rational. Nowadays, things might have changed (maybe) ... and technology alone is not the solution. IT might support collaboration and information management, but we need seriously (a timely) action of legal advisors on supporting democratic-action and intellectual-property-protection.

Nova Spivack has posted the interesting idea that the number of questions is faster growing than the number of answers. This forces us to ignore more facts, which leads us away from rational decision making, if we are not collaborating. I think, he has the point that in some areas we are facing bounded rationality in phases of a work process. On the other hand, I am willing to ignore information about the south of orion and rather read another drug design paper instead. The problem is that I can not read all 'relevant' articles (filter problem of information overload). Recently, I met Barry Hardy and he named it also the real-time problem of knowledge management. So, I have to filter, filter, and filter my priorities, which eliminates elements of making serendipitous discoveries, and puts me on risk of being not innovative and rational ignorant in my work.

Collaboration and data driven decisions seem to be a good way for creating innovative ideas. Though, I think we will need a lot of legal advice, open source environmentalism advice, and data security discussions before the drug design community can step into web 2.0 ! People do silly things sometimes, and even Google had already data leakage problems. Usually, are internal people the major concern for data leakage, not technology, or hackers. Data leakage is unacceptable for the drug design industry, where intellectual properties are critical for a drug design process of 10 to 15 years. Or we just need longer patent coverage. Some inventions take minutes, some decades, is it then fair that all patents have the same runtime?

Anyway, Shirky is right, we need to direct collaborative efforts. Just being open does not mean that questions are getting directed or answered (have seen this a lot). He mentions the do tank - the democratic action workshop - as solution? I certainly think that legal advisors should speed-up with closing more world2web and web2world gaps (terms from Alvis Brigis). Or is the information already faster growing than the legal procedures can protect it? To all the open data, open source, and open standard people out there ... I am still with you ... and as always, open must not mean free of charge, and I think we all need to get reimbursed or bound to licenses for avoiding that others are abusing our efforts.

The Do Tank targets the "capability gap" in practicing collaboration and forming groups among people who realize the opportunity for more collaborative decision-making in their governments, communities, businesses, or other organizations but do not have the experience, skills, models or tools to fulfill the potential.
We bring "democratic" approaches to bear on our design work -- democratic understood, not as political ideology, but as a way of life where people work together to pursue shared goals.

Just from the standpoint of a scientist with a limited reading capacity; help me in talking to people which know more about it!

Reflect - annotate protein and small molecule names in web pages

2009-03-16T19:50:00.008+01:00

The core component of Reflect is a consolidated dictionary that links names and synonyms to source data entries. It was created by merging the STRING and STITCH databases, and it currently contains over 1.5 million proteins from 373 organisms and 4.3 million small molecules..

The Reflect site annotates any web page provided, or installs a FireFox plug-in to allow annotation of any web page after a right mouse-click 'reflect this page'. Here an example for the tumor protein p53.

The reflect team of Reinhard Schneider is one of the Elsevier grand challenge finalists (sorry, for making the wrong statement before that they won this challenge, which is not known, yet).

Drug design paradigms - get involved in helping patients - haystacks, islands, and cliffs

2009-03-08T12:55:00.013+01:00

"The availability of pharmaceuticals must not be taken for granted. Pharmaceutical research requires expertise, a large commitment, a great deal of patience and the courage to take large financial risks. What counts is a sustainable framework in Germany (and other countries, too) as a location for the pharmaceutical industry. This is not a guarantee for economic success but is certainly at its foundation." [we get involved - vfa innovation]

"To bring personalized medicine forward, scientists, regulators, policy makers, and pharmaceutical company leadership will need to find ways to more successfully work together, adopt a new mindset, and take a networked approach to innovation—one in which we can successfully share pre-competitive information and common platforms such as biomarkers, genomic data, predictive toxicology, and serious adverse events information." [Paul Stoffels - Modeling change]

Why is drug design an island-bridging-task?

There are several drug design paradigms. Nowadays, it is more realistic to consider innovative drug design (report for 2008) as an island-bridging-task, then some of the earlier drug design paradigms, like serendipity and searching in a haystack.

What are cliffs we need to bridge?

patent cliffs - For having enough money for research and all regulatory steps for bringing a drug to the patient we need intellectual properties and patents for ensuring at least some rights on innovative drugs. Associated with a patent is the patent expiration. This means the network of inventors looses all rights for exclusivity and any other company can just use the final outcome of research efforts without reimbursing the inventors. Inventors (a pharma company) looses a lot of money when a drug goes off-patent. This challenge is called a patent cliff many pharmaceutical companies are facing in the next years. Anyway, we have to accept that generica companies are expanding, sometimes even by faking drug data.
activity cliffs - Do you think chemists and biologists can just make any chemical structure they want? Do you think that small drug modifications are having a small impact on the behaviour of a drug? No, this is - not - true, especially, because drugs are behaving differently in different species and because drug properties are not rarely inversely correlated.
science cliffs - Though people might assume that reviewed journal articles and patent procedures are free of errors, this is not the case. There are scientific journal publications with errors or patents of questionable value. Beside, usually are most journals biased towards positive results, only.
technological cliffs - In short, there is more chemical, biological, and literature information a single person can handle. Even worse, still, we are facing many non-connected data islands (data silos), often even without proper semantic or annotation.
communication cliffs - Some people, have a problem with collaboration, maybe just because of the NIH syndrome, or insufficient collaboration models?

What are bridges?

patent cliffs - We need a lot of contributors - in pharma - academia - and other institutions.
activity cliffs - We need support and research from many people to achieve and understand drug safety, especially for novel disease areas and targets.
science bridges - Discuss science on all levels, not only in peer-reviewed journals, join the science community on all levels, also by accepting, discussing, and avoiding reinventing negative results.
technological cliffs - We need technological, and especially social support for enriching and curating scientific information.
comunication cliffs - It was already shown that each single user contribution can enrich knowledge dramatically. For the chemistry area are the reason for missing user contributions not totally clear, but optimized technology and collaboration models seem to be very relevant.

so, get involved ... in funneling innovation ! Help eyeballing !

"Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it's the only thing that ever has." [M. Mead]

References

D. D. Richman, D. M. Margolis, M. Delaney, W. C. Greene, D. Hazuda, R. J. Pomerantz, The Challenge of Finding a Cure for HIV Infection, Science, 2009, 323, 1304-1307. DOI: 10.1126/science.1165706
All pictures taken from Wikipedia commons (bridge, island) and compiled with Picasa.
E. Kerns, Drug-like Properties: Concepts, Structure Design and Methods: from ADME to Toxicity Optimization, 2008.
Cueva de las Manos, hands.

Nature - mission science - join the blogosphere

2009-02-28T17:33:00.011+01:00

Nature's mission statement

"FIRST, to place before the general public the grand results of Scientific Work and Scientific Discovery ; and to urge the claims of Science to a more general recognition in Education and in Daily Life." [Nature's mission statement]

"SECONDLY, to aid Scientific men themselves, by giving early information of all advances made in any branch of Natural knowledge throughout the world, and by affording them an opportunity of discussing the various Scientific questions which arise from time to time." [Nature's mission statement]

Nature's recommendation

"More researchers should engage with the blogosphere, including authors of papers in press. ... There are societal debates that have much to gain from the uncensored voices of researchers. A good blogging website consumes much of the spare time of the one or several fully committed scientists that write and moderate it. But it can make a difference to the quality and integrity of public discussion."
It's good to blog (editorial), Nature, 457, 1058, 2009. DOI 10.1038/4571058a

See also

Six degrees of drug design
Citalopram: Derek
Hexacyclinol: Derek, and many more 1,2,3,4,5,6.

Molecules

Citalopram, InChIKey=WSEQXVZVJXJVFP-UHFFFAOYSA-N
Hexacyclinol, InChIKey=PFZFRWWDGXFULQ-VIPVPVOESA-N

Chemical suppliers - CHEMCATS and the long tail

2009-02-19T20:58:00.007+01:00

If CAS is providing the head of the chemical supplier power law function, who is providing the tail?

As pointed out by Derek recently and earlier exists there a vast amount of chemical suppliers. But honestly, how many people are taking the challenge of browsing all suppliers when looking for a chemical building block ? I would guess that more than 80% of the chemists are just using SciFinder, which is based on CHEMCATS. The really funny part is that chemical suppliers have to upload their data to CAS. So, CAS is not only charging users for the look-up of chemical substances, but they are also charging suppliers for uploading their substance data, and for keeping it up-to-date.

I am wondering, could there not be other business models? Which other services could provide the long tail of chemical supplier information?

Two potential upcoming services (in the future, not populated with information right now) could be ChemVendor and ChemSpider. Both allow uploading data, and I guess it is more a rhetorical question, if an upload of chemical supplier information could be possible in the future? In contrast, both services are looking for more data upload, user, and supplier contributions.

Here are the requirements for the CHEMCATS upload, so can ChemVendor and ChemSpider allow the same? Well, lets ask them ...

Catalog name/product list name
Chemical name
Synonyms, Trade names
Catalog Number/Order Number
CAS Registry Number - Why not InChIKeys ?
Structure diagram
Grades
Purities
Quantities available
Prices

ChemSpider allows even hosting more information, e.g. Spectra, so lets see what happens in the next months.

Anyway, which other services of chemical suppliers do exist right now?

Of course, are there much more services out there, but honestly, even if I would list all of them, who would check all of them?

Life science - Are the IT guys getting it ?

2009-02-10T19:25:00.008+01:00

"My argument is this: there is so much useful information that needs to be organized in a format that is free, navigational and easy to access. One person cannot do it alone; we all need to contribute for the betterment of science, in general. ... I’ll gladly contribute! How do we get the ball rolling?" [J]

The Realm of Organic Synthesis initiated a discussion about chemistry knowledge management, which has triggered a lot of follow-up discussions.

Mitch (blog) said that chemical informatics guys do not get it:

"What J and I (Mitch) are planning is truly awesome, but it is not going to fall within the realm of the type of collaborative work that the chemical informatics people know so well. J will announce the project in the next couple of days. I think you will find it completely awesome, or simply not get it."

I am really curious to see what they are planning. I agree with Rich that many tools and databases are of questionable use for people working on a daily basis as chemists or with chemists or biologists.

Just by accident, an (industry) colleague was asking me today, if I can not recommend an open-source tool for browsing through a SDFile, which is the de-facto standard for exchanging chemical data with experimental biological activity. Honestly, my first thought was BioClipse 2beta, which I have not looked at for months. I know that people are working on it for years, but guess what, SDFile browsing was not working for the data files I tried. As quick-fix I recommended the freeware (not open source) ChemFileBrowser. Not to forget all the commercial options I told him.
Anyway, looking forward for other recommendations I am not aware of ?
I agree again with Rich that CAS number searches are important. After the earlier discussions, we know that there is little room for fair-use as brought up by Tony. Still, at the end (of what?), the CAS guys are officially supporting Wikipedia, but is ChemPedia legal with all the CAS numbers it hosts?
PubChem: Well, lets put it that way, I know it, but how useful is it in industry? Lets face it, (dirty) quantity is not everything, but quality (ChemSpider), and most important purchasebility (eMolecules). At the end of the day, we want to create compounds, not just look at them on the screen.

Anway, J and Mitch, on the one hand you say you want to collaborate on the other hand you are not interested in the collaborative work of the cheminformatics guys. Sorry, I am confused !
I really hope you do not want to create yet-another-collaboration-online-data -silo. I must agree with Deepak, that just historically, it is unlikely that a single whatever service will make it. So, we have to think about some sort of communication between services. As I commented on Egons post, I believe in the social principles, rather trust people than technology alone. Any form of knowledge is just data, and faces the risk of creating a silo: in people, in departments, and in institutions.

Let me quote you again

"much useful information that needs to be organized in a format that is free, navigational and easy to access"

What a challenge. You can have

little structured information, easy to organize, easy to navigate
much structured information, easy to organize, difficult to navigate
much unstructured information, difficult to organize, difficult to navigate, some people call this life science, and lets not even get started with the whole omics thing ;-) Well, maybe a little !

Making it even more complicated, you mentioned collaboration, so

little contribution, easy group dynamics, no technical requirements, just take a piece of paper on a round-table
lot of contribution, difficult group dynamics, massive technical challenges ranging from communication standards to performance problems.

At the end, I see some discrepancy between "lot of information" and "easy to do", so, again, curious to see what happens next. As long as people keep talking and the ball rolling, everything is possible ...

... and some IT guys are really not getting it !

Collaboration bullseye 2.0: Information overload, filter failure, and ways out

2009-01-17T18:46:00.027+01:00

"Using the Internet today is like trying to use a library where all the books have been dumped on the floor and the lights turned out." [A.J. Vendeland, 1996]

"A metaphor I use to describe the Internet and now web, how it is organized, and how useful the stuff is that one can find there. My kitchen junk drawer has lots of good useful stuff, mixed in with lots of useless stuff that I have no need for but I have kept anyway, just in case!" [J. C. Neale]

This post has two major goals. One is to disentangle the difference between information overload and filter failure in science. The second is to show a potential way-out of both problems.

This post was triggered by a controversial discussions, saying that in science information overload IS or IS NOT filter failure.

IS - Clay Shirky started the whole "It's not information overload- It's filter failure" discussion and explains why. He brings even a scientific chemistry study group example after 15' in the video.
IS NOT - Bench Marks raises three major points against this idea in the context of science. First, filtering is already done, better than ever, but still the amount of information is too high. Second, he has concerns trusting a random group of web 2.0 strangers. Third, he thinks science will suffer, if the paper reading is left to other (not cutting edge) researchers, because they might not be good enough in connecting the innovative dots.
IS - Alan Cann says that "Conventional wisdom holds that Aristotle was the last person to know everything (Neill & Ridley 1995 The Philosophy of Art: Readings Ancient and Modern. McGraw Hill). That probably wasn't true - Aristotle was a victim of Rumsfeld's Syndrome - he didn't know what he didn't know. Certainly, every scientist since Aristotle has suffered from information overload - even if they didn't know it. So if we can't know everything, what do we have to do? Filter !"
IS NOT - Bench Marks clarifies again that scientists are already filtering like crazy, but due to the exponential growth in research and journals, there is more knowledge they can assimilate. He offers also two solutions. One is to specialize more, which seems contradictory to the cross-disciplinary world out there. Second, he recommends spending more time with reading, if scientists want to work in a cutting edge area.

Before I will start with discussing actual business trends and solutions, it is important to understand that there are two major forms of information overload. I think one of them relates more to the 'classic' information overload problem (rabbit hole risk), the other more to a filtering problem (Spigot risk). Still, both things are highly related and I am not sure if any differentiation is of much use. At the end we need to tackle both problems, e.g. by collaborating.

Rabbit hole risk of information overload

"Suddenly she sees a white rabbit run past wearing a coat and carrying a watch, lamenting running late. She follows it down a rabbit hole and falls very slowly down a tunnel lined with curious objects." [Alice in Wonderland]

In short, this is the effect of highly non-linear information, e.g. encyclopedias, web pages with links, and scientific papers with references, which allow you do get easily distracted by more and more information, e.g. via links.

I stopped counting the number of deja-vu's experiences, where I was facing this effect. I started with a single scientific article, and was ending up with an even larger number of papers on my reading list afterwards. Especially, review articles have usually more references I put later on my to-do list. So, this phrase 'follow the white rabbit', is not only something for computer geeks, but also a well-known information overload risk to knowledge and science junkies. Honestly, when thinking about cutting edge scientists, then my guess is, that they are more stressed than others, just because they are always trying to stay close to the scientific oligarchy. People which have stopped trying to catch up, do not care if they do *not* read five or twenty articles, news alerts, or encyclopedia entries.

Spigot risk of information overload
The spigot risk is that the incoming scientific information flow is not only continuous, but also exponential, while filtering and reading capacity is usually not.

I would bet that anyone who tried to stay competitive has faced this problem already, especially people with the above mentioned rabbit-hole-syndrome. To get out of this dilemma, I rather trust people, than any technical solution (alone) to filter, rank, and annotate information, e.g. eMail alerts, or RSS streaming. This is also one reason why all the systems out there should allow a collaborating human factor to allow positive networking effects.

Why social software might be a solution
The evidence for this can not only be found in the wisdom of the crowds on Wikipedia, but also in several top web enterprises, just look at the top 500 web companies and how many of the top-scorers are based on social software elements.
As highlighted by Andrew McAfee (blog) is the key element to trust people at some point, which is definitely better than trusting reading or paper writing bots. He is showing how important it is to keep social networks on several levels (they are ranging from tight, weak, potential, to none). This is the major key for understanding the resulting collaboration forms based on this collaboration bullseye.

Again, why should we care?

Should we not only rely on internal knowledge and closed groups? Actually, no ! We know already the problem of bounded rational drug design. We also know that "the problem of a rational economic order is… that the knowledge… of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge" [F. Hayek].
In other words, nobody expects that a single person knows everything.We know that people are getting more specialized and that we are facing a wide range of diverse cutting-edge experts. Guess what, I think this is exactly what we need, but not on all collaboration levels. Andrew McAfee confirmed this also with the numbers of problems solved by InnoCentive. The numbers of problems solved was usually higher, if the diversity of the scientific contributors was higher. I must admit I was amazed to see that you can find a lot of chemical and life science challenges on InnoCentive (@WP).

Finally, we have to trust other people, and they are not all random, some are already in a tight or at least weak collaboration and information network with you. Sorry, Bench Marks, I would rephrase "To a hammer the world looks like nails" to

"I am a scientific nail, but I trust a hammer often. If not, I want to be close to other scientific nails. Lets make sure most nails are strengthening their networks and collaborations, not only their (closed) knowledge levels and data silos."

I would highly recommend to follow McAfee's talk after (47:51), because he addresses also the intellectual property conflict question (or how open should we be?) and mentions highly respected life science companies like Pfizer, Eli Lilly, and InnoCentive.

Beside all the things already discussed, I also mention things I find useful for finding, filtering, collecting, and ranking information.

Searching - I am using iGoogle as my personal homepage, and I have created a few gadget for helping me, but I am also using things, which were already available, e.g. a RCSB search.
Collecting - Since I am not the only one loosing time in finding relevant information, I use my very personal mixture of content management systems (CMS) like MindManager, XWiki, and a LaTeX/BibTeX solution for keeping track of my read articles.
Social networking - Well, the people which know me, are fully aware, that I like communities and that I appreciate community efforts. I think everybody, which tried to get community things going knows that it is not always easy to reach a tipping point. Some previous posts might highlight this:
- editorial, social, or both - again, I believe in Linus' law: given enough eyeballs, all bugs are shallow
- a community moves on - well, also networking costs time
- six degrees of drug design - I believe in diverse within six steps
- quality improvements are always possible
- Finally, I am working for a pharmaceutical company, so I am open, but not stupid. Anybody in this industry knows that intellectual property protection, project work, external collaborators, and any target activities are highly confidential. So, if you are working on such things, like I do, do not expose this in any form on any typical open social network tool like your blog, Facebook, Twitter, FriendFeed, Plaxo, whatever (see cons of social networking or listen to Clay Shirky's friend story). If you want to talk to people, follow the legal rules and policies !

References/Credits

via ibiblio Libraries FAQ: "Using the Internet today is like trying to use a library where all the books have been dumped on the floor and the lights turned out." A.J. Vendeland, in Review of The Software Publishers Association Europe Conference, Conference Analysis June 28, 1996.
Image: Down the rabbit hole @Wikipedia, modification was created with GIMP and is available on request.
Image: FlowingTap @Wikipedia, modification was created with GIMP and is available on request.
Image: Six degrees of separation @Wikipedia, modification was created with GIMP and is available on request.
The two forms of information overload on DigitalNatives were found via Strangers On The Net.

Antithrombotic graph visualization - Chemistry 2.0 and the ChemSpider journal

2009-01-04T15:37:00.024+01:00

"It’s not Information Overload. It’s Filter Failure. If you have the same problem for a long time, maybe it is not a problem, maybe it is a fact ... some of it is rethinking social norm ... we have continuously to improve filters, that just broke !" [Clay Shirky]

"We intend to demonstrate how modern web technologies can be used to dramatically enhance the type of information that can be communicated using web-based tools over standard online publishing approaches." [ChemSpider journal (editorial board)]

Inspired by the story about the chemical-study-group example of Clay Shirky (after minute 15), I got again reminded about the information overload problem. I agree totally with Clay that a long persisting problem, encourages a desire to get it changed. As shown by Tony from ChemSpider does the life science community have the (technical) ability to enrich chemical data.

So, are there just (social) barriers left? According to VonHippel do single users rarely show enough investment into innovations, if they think that this is only their single problem, but not the problem of a larger community (see chapter four). But wait, I was always assuming that the community is having this problem together, not only some single people living with their data silos.

Again, and in-line with Clay and VonHippel, are there good reasons to rethink social norms for improving information filter processes, e.g. the ones with buried, wrong, hidden, or shrouded data.

Now, lets have a look at an antithrombotic drug design data example, based on community web communication.
I used the Fontaine et al. factor Xa data set (EC 3.4.21.6) from cheminformatics.org and enriched it with PDB ligand structures (final structure file). Factor Xa inhibitors are inhibiting the trypsin-like serine protease, involved in the blood coagulation process.

What has this data now to do with drug design? One very reasonable question could be

Which XRay ligands are closest to the Fontaine et al. structure-activity relationship data for allowing structure-based drug design?

In short, there might be many ways to give an answer. Here, we are now interested in checking the functionality of ChemSpider and blue obelisk tools, e.g. Pybel. The idea is also to move towards web-based and scientific collaboration models, e.g. via google docs.

First, since I strongly believe in the benefits of curated data and community efforts, I was checking how much factor Xa data was already contributed and curated within ChemSpider. Honestly, I was facing a couple of challenges (or filter failures) by trying to answer this question. It is already easy to upload data to ChemSpider, but it is less efficient to find out, if those molecules are already registered there? Please mind, that especially people in industry have to protect intellectual properties and can not always upload data immediately, but maybe contribute on a technical level or after patent expiration. Based on the InChIKey (1, 2, 3) it was possible to create an InChIKey2CID script. Though, this web-based retrieval is still extremely slow and suboptimal, and certainly not usable for thousands of molecules. Nonetheless, none of the factor Xa molecules was uploaded to ChemSpider, not even the PDB ligands (see flat file result). So, still some work to do in the future ;-)

Second, for now ChemSpider supports mainly a quick similarity check on InChIKeys, neglecting stereo information. Strictly speaking is this only one very specfific way of a similarity search. In other words, users are left with the option for a PubChem search, which covers also the ChemSpider data. Maybe there exist already a web-service for returning for a single molecule query the most similar or the most five similar compounds, but I have not found out how.
At the end, it was easier to follow Noels Pybel examples (general, fp, sd) and creating a molsim2gxml script. The script will not only calculate fingerprint similarities between molecules, but also an XGML output for the data visualization.

Xa ligand similarity (top 5 compounds).
Original graph files (yEd): gml, graphml, xgml, svg.
Tulip acccepts also the GML format.

Xa ligand similarity (best compound, most similar).
Original graph files (yEd): gml, graphml, xgml, svg.
Tulip acccepts also the GML format.

Finally, we obtain two graphs showing the similarity (connectivity) of the factor Xa SAR training data (blue), test data (green), and the XRay data (red). Each node represents one molecule, each edge links either the most similar compound (lower graph), or the five most similar compounds (upper graph).
In the lower graph, we see that only two XRay structures (indicated in red) are close to the factor Xa SAR data in the training set (indicated in blue). One is a zinc cation, which is in this case not interesting for ligand design, the other is the ZEN-PDB ligand. This ligand can be found in the PDB structures 1j17, 1ql7, 1ql8, 1ql9, and 1v2k, which could be a good starting point for structure-based drug design (SBDD) and structure-property-relationship (SPR) transfer. On the other hand can we see that the overall distance between the Fontaine SAR data (blue and green) and the XRay data (red) can be easily spotted. So, be warned when trying any SPR transfer, and we have not even started talking about 3D ligands and the protein flexibility !;-)

So, yes, some of the public SAR data can be linked to Xray information and could support structure-based design. Anyway, a fast answer to the original question took some time. And could this now be uploaded to support the drug design community and avoiding this kind of filter failure problem for other community members? I am not aware that the EBI, or NCBI services allow data upload.
On the other hand, would ChemSpider and the ChemSpider journal (editorial board) benefit from additional services, like fast InChIKey checks, or similarity searches.

References

molsim2xgml python script
inchikey2cid python script
yWorks - the diagramming company
F. Fontaine, M. Pastor, I. Zamora, F. Sanz
Anchor-GRIND: Filling the Gap between Standard 3D QSAR and the GRid-INdependent Descriptors
J. Med. Chem., 2005, 48, 2687-2694. DOI 10.1021/jm049113+
N. M. O'Boyle, C. Morley and G. R. Hutchison
Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit
Chem. Cent. J. 2008, 2, 5.
DOI 10.1186/1752-153X-2-5
R. Guha, M. T. Howard, G. R. Hutchison, P. Murray-Rust, H. Rzepa, C. Steinbeck, J. Wegner, E. L. Willighagen
The Blue ObeliskInteroperability in Chemical Informatics
J. Chem. Inf. Model., 2006, 46, 991–998
DOI 10.1021/ci050400b
D. Rauh, G. Klebe, M. T. Stubbs
Understanding Protein–Ligand Interactions: The Price of Protein Flexibility
Journal of Molecular Biology, 2004, 335, 1325-1341.

10.1016/j.jmb.2003.11.041

See also

Six degrees of drug design, blog post.
Innovation funnel, blog post.

Drug designers are creating innovative ideas

2008-12-20T15:54:00.005+01:00

"The FDA will approve at least 21 first-of-a-kind drugs this year, up from 18 in 2007. And the number of black-box warnings decreased by 25 percent. The boxed warnings are the most serious a drug can carry." [salon.com]

It is well known that researchers in pharma are facing challenging times. Nonetheless, the FDA has increased the number of approved drugs last year (21 drugs compared to 18 in 2007). This in combination with the increasing innovation in pharmaceutical research gives hope for the next years.

The question for me is, can this industry still be innovative with all those layoffs, and still make it a mission possible for the next years? I know that some people think that technology can help

"The constant march of technology makes it possible for a smaller work force to do virtually the same job as a larger workforce." [10k Google layoffs]

Honestly, this sounds reasonable for the chip and software industry, but does this also work on scientific matters, especially for drug design? If we keep thinking and are ready for change, this might be possible.

See also

Group Contribution revisited (via Fragment Based Drug Design)

2008-12-17T20:33:00.003+01:00

"What chemists really need is reliable data, and biophysical techniques" [FBDD events 2009]

(via practical fragments)

In the world of fragment-based ligand discovery, researchers hope that two fragments, when linked together, will behave at least additively: the free energies of binding for each fragment will sum together, with a multiplicative effect on affinity. In ideal cases, linked fragments will behave synergistically. But all too often, linking two fragments produces disruptive behavior, and the resulting molecule actually binds less tightly than would be predicted based on the binding energies of the individual fragments. This occurs not just when linking fragments, but in fragment merging and growing as well. Can such phenomena be modeled?
The mathematical groundwork was described more than forty years ago by Spencer Free and James Wilson at the old Smith Kline and French company, and came to be known as a Free-Wilson analysis. In a nice update of this work, Julen Oyarzabal and co-workers have applied this technique to ... a variety of different targets, including a kinase, GPCRs, ion channels, and P450s.
...
But even if synergy is elusive, the paper suggests that you’ve got a better than even shot of producing a whole that is at least equal to the sum of its parts.

For an excellent pragmatic review about FBDD see also the comments in FBDD events 2009.

Mining clinical trials

2008-12-15T20:06:00.007+01:00

"This (clinical data mining) application enables rapid extraction of information about institutions, diseases, clinical approaches, clinical trials dates, predominant cancer types in the trials, clinical opportunities and pharmaceutical market coverage." [10.1186/1745-7580-4-7]

(via open access news)

In 2007 more than seven million people died from cancer. This means there is still a lot to do for helping patients and for reducing this number. This is also the reason why each new treatment option has to follow good design criteria and sufficient testing, called clinical trial design. Typical design criteria could be:

assess the safety and effectiveness of a new medication or device on a specific kind of patient (e.g., patients who have been diagnosed with Alzheimer's disease)
assess the safety and effectiveness of a different dose of a medication than is commonly used (e.g., 10 mg dose instead of 5 mg dose)
assess the safety and effectiveness of an already marketed medication or device for a new indication, i.e. a disease for which the drug is not specifically approved
assess whether the new medication or device is more effective for the patients condition than the already used, standard medication or device ("the gold standard" or "standard therapy")
compare the effectiveness in patients with a specific disease of two or more already approved or common interventions for that disease (e.g., Device A vs. Device B, Therapy A vs. Therapy B)

Today, at the 2008-12-15, are there 20292 clinical trials with a cancer treatment published, some are recruiting, some are ongoing, and some are completed. This information can be accessed in XML format (DTD) (please read terms and conditions) for allowing an easier data mining on those large data sets. One method is for example the ability to browse through cancer vaccine trials via SpotFire, a business intelligence tool.

Other options could be to cross-link this information, e.g. by using crowd-sourcing concepts (Crowdsourcing@Wikipedia).

References

X. Cao, K. B. Maloney, V. Brusic, Data mining of cancer vaccine trials: a bird's-eye view,
Immunome Research 2008, 4. doi:10.1186/1745-7580-4-7
Linking and downloading of ClinicalTrials.gov data. Please read Terms and Conditions.

Public data sets on AWS (Amazon Web Services)

2008-12-06T17:43:00.007+01:00

"AWS hopes to provide researchers across a variety of disciplines and industries with tools to enable more innovation, more quickly" [Public data on AWS]

If you are interested in cloud computing and storage then you will be delighted that Amazon offers support to the science community. Though appreciated, do I think that the information provided for the data sets is very sparse, e.g. which conformer generation was used for the chemistry data sets? How exactly was the UGI dataset created?

Anyway, this sounds similar to the computing and storage solution of Google, called MapReduce and GFS. I do not know enough about it to understand the technical differences between the frameworks of Amazon and Google, but both are used to work with tons of data. As announced by Amazon would users (or companies) only have to pay for their user storage and computing time, but not for the public data. This is fair, but I was also wondering, if people could buy technical support as well?

BIOLOGY

Annotated Human Genome Data provided by ENSEMBL - An annotated form of the Human Genome, perfect for biological research
GenBank provided by the National Center for Biotechnology Information – COMING SOON - An annotated collection of all publicly available DNA sequences including more than 85.7B bases and 82.8M sequence records
UniGene provided by the National Center for Biotechnology Information – COMING SOON - A set of transcript sequences of well-characterized genes and hundreds of thousands of expressed sequence tags (EST)

CHEMISTRY

A 3D Version of the PubChem Library provided by Rajarshi (blog, homepage) - A 3D (single conformer) version of Pubchem, a public database of chemical structures in SD Format
UGI Virtual Conformer Library provided by Rajarshi (blog, homepage) - 80GB of data in SD format on conformers for 500,000 molecules that can be used for virtual screening
PubChem Library provided by by the National Center for Biotechnology Information – COMING SOON - A data set of information on the biological activities of small molecules

(via Egon@del.icio.us)

Python Training for Cheminformatics and Modeling

2008-11-09T16:23:00.008+01:00

"the best choice of high-level programming language for computational chemistry is Python" [A. Dalke]

Many cheminformatic/modeling toolkits provide Python interfaces, as shown in the summary below. This is a good reason to consult Andrew for a training sessions, or checking Andrew's or Noel's blogs from time to time. The various open source projects, from which a few are stalled, support Python, as well as the academic/commercial OEChem toolkit. Two very important modeling programs supporting Python are Schrödinger's modeling suite and PyMol. For a training on the last two programs, you best contact the software support teams directly.

Please let me know, if other molecular modeling/ cheminformatics/ statistical modeling programs should be mentioned here !

[source: EuroQSAR poster, A. Dalke, 2008, URL, PDF]

Support the star of life 2.0

2008-11-08T13:49:00.007+01:00

Imagine a world in which every single person on the planet is given free access to the sum of all human knowledge.
— Jimmy Wales, Founder of Wikipedia

Imagine the sum of all natures knowledge can maintain and restore health by the prevention and treatment of illness, as symbolized by the star of life.
— free interpretation of medicine and web 2.0

Data, models, or both?

2008-08-03T20:01:00.006+02:00

Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. [C. Anderson, Wired, 2008-07-16]

First, I disagree with this statement. Second, thanks to bioinfoman3@delicious for sharing this information.

Honestly, I do not get it, why are people claiming that the scientific community, e.g. drug design, is similar to the chip industry or google concepts. Here, Chris Anderson claimed that data alone will replace theoretic concepts. As said by others, in the comments to his article, is data alone not information. Amund Tveit confirms this by showing that data correlations alone might be misleading, because you can find correlations in everything, even if it makes no sense. Data means any data, so if you look-up NME in google then you will not find a new molecular entity as first hit, though this it what I was expecting. So, I have serious doubts that this kind of thinking is supporting science at all, since it might lead away from the right path.

In the drug design area or other domains with chemical and biological information it is well known that the raw data web is not ready or, at best, suboptimal for chemistry. Let us assume that we have already fully equipped and finished data curation, and ontology projects, which provide information, not only data. Does this information-rich data help making new drugs without any models? I doubt so! Beside, the curation and ontology projects are far from being finished, so I doubt so heavily ! We have to accept that any data set will be always incomplete or noisy, even if clean music, book, gaming, and video data would be added. Would that support drug design or other scientific areas?
As said earlier

Just accept that we are working in an area where the chemical space is just too large for allowing us to get the complete picture of it. Thus, embrace incompleteness (bounded rational drug design). [Six Rules For Creating a Data Driven Drug Design Project, Mining Drug Space, 2007-11-02]

I completely agree, that

All models are wrong, but some are useful.. [G. E. P. Box]

By the way, if Chris Anderson is stating this at the very beginning of his article, why is he then trying to neglect the usefulness of models afterwards?

If people keep claiming that they know how to change science positively, then I am wondering, if they know that data generation itself might be a serious bottleneck. In drug design some effects, e.g. ADME and toxicity, are really hard and expensive to measure. Believe me, if the model disbelievers tell us that we have already enough data and models for understanding oral drugs in humans, then anyone in the drug design industry would be happy to not only reduce, but stop, human clinical tests and provide life saving drugs to patients immediately.

Finally, I strongly believe, that we need as much connected and information-rich data as possible (so far I align with C. Anderson), that we need good models for supporting decision making processes and saving time and environmental resources, and that we need a combination of both for innovative scientific thinking and novel developments.

Quotes from the neighbors (July 2008)

2008-07-27T21:27:00.000+02:00

==Receptors: Can't Live With 'Em, Can't Understand 'Em (In the pipeline)==

At various points in my drug discovery career, I’ve worked on G-protein-coupled receptor (GPCR) targets. Most everyone in the drug industry has at some point – a significant fraction of the known drugs work through them, even though we have a heck of a time knowing what their structures are like.
For those outside the field, GPCRs are a ubiquitous mode of signaling between the interior of a cell and what’s going on outside it, which accounts for the hundreds of different types of the things. ...

==A-β Dimers- The Long-Sought Minimal Culprit in Alzheimer's Disease? (The Curious Wavefunction)==

Following on the heels of the headline-making Nature publication that demonstrated that NSAIDs (Non-steroidal AntiInflammatory Drugs) uniquely targeted a substrate (APP) rather than an active site of the gamma-secretase complex involved in plague formation in Alzheimer's (see Discount Thoughts for a great summary) comes a paper that may turn out to be one of the important papers in the history of Alzheimer's disease (AD) research.

==NSAIDs bind to amyloid-β (Discount thoughts)==

One of the best-known features of Alzheimer's disease pathology is the formation of proteinaceous amyloid plaques in the brain. In Alzheimer's disease these plaques are primarily formed by the amyloid-β peptide (Aβ) derived from the amyloid precursor protein (APP) by the action of β- and γ-secretase. The length of the Aβ peptide varies, but the 42-residue form (Aβ42) is more likely to form plaques and fibrils.

WikiPathways - the challenge of community curation

2008-07-26T20:38:00.001+02:00

"But as the influx and complexity of biological data continue to grow, so will the challenge of organizing and maintaining these databases.
Fortunately, the biology community can provide an answer that will scale with the challenge: community curation." [DOI 10.1371/journal.pbio.0060184]

WikiPathways follows the concept of a community based data curation principle. So, they follow the same strategy of tools like

The database is available as SubVersion repository (SVN) in the GenMAPP Pathway Markup Language (GPML) format. For a user friendly data editing PathVisio is used.

References
A.R. Pico, T. Kelder, M.P. van Iersel, K. Hanspers, B.R. Conklin, C. Evelo, WikiPathways: Pathway Editing for the People, PLoS Biol., 2008, 6, e184. PMID: 18651794. DOI 10.1371/journal.pbio.0060184

OpenAstexViewer - an open structural biology viewer

2008-06-19T19:58:00.006+02:00

As posted by Noel and Rich was the Java-based AstexViewer just got LGPLed and voila! Here it is, the OpenAstexViewer for structure-based drug design.

I personally was especially interested in the electron density functionality and how easy it would be creating a view on an enzymatically active-site.

As example I used Lisinopril an ACE (Angiotensin-converting enzyme) inhibitor. This class is seen as one of the success stories of rational drug design, based on the structural biology of carboxypeptidase A and medicinal chemistry.
My first thought was using Captopril, the initial drug for this class, but I could not find an electron density map and had to pick another example. Since the PDB structure (2C6N) of Lisinopril has a deposited electron density map this was now used.

After downloading the program and loading the structure and the map, I needed only a few mouse-clicks for selecting the ligand and creating a view on active site surface. Here is the result.
The drug is shown in the active site as sticks using atom coloring with having the zinc ion on the left in gray. The active site surface shows the lipophilicity of the protein residues. In orange we see the electron density map, which indicates a reasonable binding mode of this ligand.

Finally, but a little off-topic, I was also curious, if LASSO on ChemSpider identifies this molecule as an ACE inhibitor? In short, yes this target class gets one of the highest scores. Nice, having some online tools around for collecting quickly drug information.

After all of this I must say that the OpenAstexViewer is a very elegant, easy to use tool, which is extremely powerful. Has anyone tried already the scripting abilities ?

References

M. Adam, Integrating research and development: the emergence of rational drug design in the pharmaceutical industry, Stud Hist Philos Biol Biomed Sci, 2005, 36, 513-37. PDF.
PMID: 16137601
H.R. Corradi, S.L. Schwager, A.T. Nchinda, E.D. Sturrock, K.R. Acharya, Crystal structure of the N domain of human somatic angiotensin I-converting enzyme provides a structural basis for domain-specific inhibitor design, J Mol Biol, 2006, 357, 964-974. PMID: 16476442

Science - editorial, social, or both

2008-06-01T21:10:00.002+02:00

It was almost two months ago that David Bradley asked the question (via LinkedIn), if science may benefit from social software? Several people responded, and especially David Crotty started a controversial discussion based on my raised points.

First, Crotty said that 'popularity' is a terrible measure of quality. Actually, I think I agree on this! Otherwise, any popular web page would be automatically one with a higher quality. I have some software engineering background and would like to take this into account. I would argue that not only the access rate of web pages, but also the cross-linking character between them, or the number of errors per page would be good metrics. Was anyone following the dispute about the Britannica-Wikipedia comparison (comment)? Again, what should we compare? Error rate in articles, access rate, or cross-linking rate? Do we not have the same problem for journal impact factors? I think we have to accept that any model (or metric) will never represent the full truth about reality, but it might give us some additional rational for making decisions. Are any molecular modellers still with me? Yes, then I have a question. Do you believe that this metric discussion about molecular docking and scoring will ever end? If you think so, what metric or benchmark data set will lead to a fair comparison? I do not think that this is possible, but I strongly believe that the metrics and benchmark data sets around will help us to improve over time. Even using wrong metrics does sometimes help improving metrics, and therefore rationality.

Second, I said that "I cannot see how any editorial process can cope with this (information overload) problem"; he asks the question "Is he (means me with that) implying that journal editors are not 'really interested' in the papers they’re reviewing?".
Not at all! The point I tried to make is that the editorial process will assign reviews to reviewers, which will in most cases do the reviews also in their spare-time. So, at this point it does not matter, if reviewers get assigned by editors or by any other social web system. I think my point of view is not really strong on this, but I believe that missing transparency might be a major source of mistakes of any process, e.g. a peer-review process (via spreadingscience). I am not saying that transparency can avoid mistakes, but a transparent process would allow democratic voting systems, which is one type of decision making on Wikipedia. Anyway, in cases where no decision can be reached by user votings or reviewer comments, the next step will be taken by a Wikipedia mediator or an editor. The major difference is that in one case the editor has to do the assignment, while in the social software model the dedicated people would come to the editor automatically. And especially in cases where no editor is in place, users would just start editing (be bold!),till one or several users get promoted as pseudo-editor.Third, David Crotty says that "you need a better editorial oversight, not less" and he cites Rob Malda (via Wired) "When you're building a system like this (means Slashdot) you're balancing the wisdom of the crowds versus the tyranny of the mob. Sometimes a crowd is really smart, but some things don't work so well by committee. Crowds work when you have a tightly knit group of people with similar interests ..."
Well, again I agree that you need rather more information than less. Though, I would like to challenge this a little. Where is the editor getting his information from? I would assume that he will use at least some social web services or (social) cross-links in journal publications for finding other experts.

I was never a friend of black and white views. I would conclude that

any editorial process without some social software will run into an information overload problem, and you should not do all of this alone, but share workload.
any social software process without editors runs into an information overload problem, and how can you discriminate information from noise? You need experts, editors, and some people call this ontology knowledge, or the semantic web.
a combination of both, editorial expert knowledge in combination with social software is promising for creating massive knowledge networks, e.g. WikiProteins. Finally, any technological process will fail, if the people behind it, are not dedicated enough, run into workload problems, or are hold back by unclear intellectual property situations. So, whatever you do, do it in a team and make it a transparent process. If required, put some license, copyright, intellectual property, or whatever restrictions on it, so make steps towards users and do not wait till users will adapt to your user scenario. In all cases, avoid destroying information and allow a cross-linking between information.
As already said on ChemSpider, "get the hell organized..." and talk to each other.

Finally, David Crotty and David Bradley, thanks for discussing this in an open-minded setup and via a social software system. I am looking forward to more news, questions, answers and controversial discussions ... at the end it is all about learning and helping people, right?

WikiProteins - May the community be with you

2008-06-01T12:32:00.002+02:00

"We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery. The system is available for beta testing at wikiprofessional.org" [DOI 10.1186/gb-2008-9-5-r89]

The author list is impressive (e.g. Jimmy, Prof. Ashburner), as well is the number of communities and organizations (PubMed, Google, Yahoo, UniProt). Of course is this not a guarantee for success, but at least is it interesting that those people have started talking to each other. I guess they all want to tacke a challenging scientific problem, which is creating knowledge out of information noise. Very impressive, indeed!

I created an account and the system looks very beta at the moment. This early release strategy is very normal, at least for Jimmy, which follows the release soon, release often paradigm of open source communities. When Jimmy released Wikia, he got also some negative feedback, because some people thought it was too early releasing the system. Anyway, this has not stopped us from founding the Life Science Group on Wikia, though I admit that not too many people have contributed so far. I hope that the WikiProfessional system will be able collecting enough critical brain mass for getting a good head-start.

"The first release of WikiProteins contains an embryonic version of what is intended to be developed into a fully functional WikiProfessionals in 2008 and beyond. Users are able to review their pre-constructed (recent) publication list and create their Knowlet before registration. With an increasing number of authors having curated their own Knowlet(s) in the system, creating communities of expertise and indicating their availability for comments and peer review, instant messaging and web conferencing will become available in the system." [DOI 10.1186/gb-2008-9-5-r89]

The system highlights three major use cases:

Community annotation: The basic principle of community annotation is that computers and experts interact in an iterative process of mining and curation.
Knowledge browsing: This will allow users browsing through the concept space of interesting relationships.
Collaborative knowledge discovery (example): When the connections in the concept space around antimalarials and tegafur are explored further, it becomes immediately obvious how logical it would be to reason that tegafur might indeed inhibit growth of malaria parasites, at least in vitro.

May the community be with you ...

Reference

Article (macmwdomchmpplbmbwmmrbb08)
Mons, B.; Ashburner, M.; Chichester, C.; van Mulligen, E.; Weeber, M.; den Dunnen, J.; van Ommen, G.; Musen, M.; Cockerill, M.; Hermjakob, H.; Mons, A.; Packer, A.; Pacheco, R.; Lewis, S.; Berkeley, A.; Melton, W.; Barris, N.; Wales, J.; Meijssen, G.; Moeller, E.; Roes, P.; Borner, K. & Bairoch, A.
Calling on a million minds for community annotation in WikiProteins
Genome Biol, 2008, 9, R89. DOI 10.1186/gb-2008-9-5-r89. PMID 18507872
Six degrees of drug design, Mining Drug Space, 2007-08-17.
Six sigma in drug design, Mining Drug Space, 2007-09-15.

Lasso, docking, and protonation

2008-05-17T22:45:00.000+02:00

The SimBioSys blog posted several literature references and discussions about protonation states

CAS numbers are not public domain, are they?

2008-03-08T10:00:00.011+01:00

"Work created before the existence of copyright and patent laws also form part of the public domain. The Bible and the inventions of Archimedes are in the public domain. However, copyright may exist in translations or new formulations of this work." [Wikipedia]

As posted by Tony is the Chemical Abstract Service (CAS) discouraging using their CAS services for assigning correct CAS numbers to structures for any third party database. Wikipedia is a source of structures, which is public domain due to its GNU FDL. Still, this does not imply that any translation of structures, e.g. CAS numbers, are in the public domain, too. Honestly, this raises a serious problem for curating CAS numbers on Wikipedia and this raises indeed the question, if they should not be dropped from Wikipedia, and any other information source, at all? Is it not better having no information, than having wrong information?

A CAS number is for me only one certain translation of a chemical structure. In this case, the only source and creator for CAS numbers is the American Chemical Society. CAS claims that their services can not be used for curating other data sources. Does this also mean that people can not use CAS numbers from publications?

"The public domain can also be defined in contrast to trademarks. Names, logos, and other identifying marks used in commerce can be restricted as proprietary trademarks for a single business to use. Trademarks can be maintained indefinitely, but they can also lapse through disuse, negligence, or widespread misuse, and enter the public domain. It is possible, however, for a lapsed trademark to become proprietary again, leaving the public domain." [Wikipedia]

And does this also mean that scientists are basically not allowed publishing CAS numbers and structures in scientific publications? How can CAS numbers then be used at all, if we can not store this information?

Many questions, and who will answer them? And, if they got answered what is the way forward for getting curated structures within the public domain? I would say (again, this time in nicer words): 'get organized scientists worldwide!' If CAS can do it, we can do it? It may take longer to get the party started, but if we do not start it will never happen.

"An ideal collaborative resource would be designed for large-scale data mining, contain curated historical data, and have data standards and deposition tools that could constantly bring in data from the published literature. ... In other words, the party might take longer to get started than hoped for, but it should be worth the wait." [M. Baker, DOI 10.1038/nrd2148]

See also

Molecule mining review, 2006

Yet Another Life Science Informatics (YA-LSI) networking portal

2008-02-25T19:49:00.002+01:00

There are yet another two new social Life Science Informatics (LSI) networking portals. In contrast to the already available LSI networking services are those technologically very mature and offer varieties of contribution and sharing possibilities for users.

Wikia: Life Science Informatics
Plaxo: Life Science Informatics

Both networking groups are looking for a stunning LSI logo, so please feel free making any suggestions.

Social search in drug design and what is Wikia

2008-02-17T20:07:00.004+01:00

Social search engines, another kind of social software, are trying to include user knowledge for improving search results in the future. One example is Wikia with an announcement of Jimmy Wales

Search is part of the fundamental infrastructure of the Internet. And we are making it open source.

In contrast to the problem described by randfish might this improve search quality and reduce search engine tweaking requirements by users. Sometimes users have e.g. to multiply search terms or to find other tweaks, for getting the search results you want. Early initiatives like the the open directory project DMOZ (@Wikipedia) are using already user organized classification schemes for web links. SWiK organizes open source project information in a Wiki-like manner.I am still a little bit puzzled to understand what the actual difference to social bookmarking systems is. Are social bookmarking services not already ranking the relevance of web links?Lets do a test on some drug design search terms. I added the tags E(experts/users), R(anking), M(ini information page). Here a '+' indicates that this feature is available a '-' means that this feature is disabled at the moment (e.g. in Wikia) or not available (yet).

Term	Wikia (+-E,+-R)	del.ico.us (+E,-R)	Digg (+E,+R)	Technorati (+E,+R)	CiteULike (+E,+R)
Data mining	36247-M	yes	615	14630	496
Drug design	34964-M	yes	285	14966	493
Bioinformatics	16937+M	yes	75	4698	496
Structural biology	7925-M	yes	60	1758	499
Organic synthesis	4403-M	yes	15	1979	498
Medicinal chemistry	3216-M	yes	45	1686	498
Kinase	2843-E-M	yes	30	1248	498
Molecular modelling	2261-M	yes	30	208	491
Cheminformatics	460+M	yes	1	147	60
GPCR	252-E-M	yes	1	121	497

Wikia is still an alpha version and user ranking of links seems to be somehow available, but does not work (yet). Though, I like the general appearance and hope to see there some new features soon. Using del.ico.us as search engine is just useless in my opinion, because I have not found any way ranking the results. Digg might be in theory useful, but lacks scientific content. Technorati is rather a news alerting service than a search engine. CiteULlike points towards scientific articles, but why are all categories delivering less than 500 hits here?

Pipeline pilot: Student edition

2008-02-11T00:26:00.001+01:00

As posted by Richard Compton on CCL.net provides SciTegic (part of Accelrys) a student version of Pipeline Pilot (PLP), which is free for academics ! I seriously like the thinking of those SciTegic guys, they are sharp, critical, and very trendy. This is a good starting point for having a hell of a discussion about science. Smart move, which is following the integration principle !

This shows another fantastic example of a smart licensing strategy, which will probably always be critically discussed in the drug design area? Just recently there was a discussion on CCL and I commented on it. Anyway, this particular case will support science and allows scientists integrating their tools on a global level. Furthermore it allows sharing tools with industrial partner without forcing them creating an additional interface layer.

Beside, is this really kicking-ass news for everybody involved in KNIME, since people in academia can now support PLP directly (if they want). KNIME provides already lots of toolboxes, but compared to PLP this number is still small. Just check them out and especially the high number of different ways of integrating data, processing data, and integrating external tools with different languages.

Chemical webs, nets, and spiders

2008-02-04T23:38:00.000+01:00

I love it, when people come together and try to understand why particular problems exist. In September 2007 we tried contacting several people for solving the problem of having proper chemical indexing or/and search services within Wikipedia. Some people replied, but there was still this strange feeling that there is not only one responsible person, but many. How can you adress a problem if nobody is really in charge and how to take decisions in such situations? Well, maybe spreading the news had some effect, because some things happened since then.

Tony (aka ChemSpiderMan) had a discussion with Walkerma from the chemistry group on Wikipedia. It turned out that both share some common interests, which is providing curated chemical data. This is a noble goal and as summarized not an easy one, especially not because there exist already some chemistry boxes at Wikipedia.
Tony and some other people from the advisory board are showing interests in creating WiChempedia, which has also one major goal, "what can be done to facilitate delivery of quality information to the chemistry community?".
Walkerma initiated a regular discussion on Wikipedia (via IRC) for helping to understand the technical and scientific issues for incorrect chemical information on Wikipedia. Though I must say: I do not like the meeting time, which is during business hours and not practical for me.
Tony is not only moving on with extending his net of dedicated persons, but also attracting new chemical sources, and spreading publications. Looks like he is a professional advocat and knows what he is doing.
Beside all of this has Chemspider itself updated and extended his structure deposition process (PDF slides), which allow people curating data there directly.

I am usually not easily impressed, but I must say that what was done so far is a great piece of work. I do not know what others think about it, but knowing the pros and cons of social networks, it is fun to see that ChemSpider is moving with its net, and not being hold back by it !

ChemSpider search on iGoogle

2008-01-06T01:09:00.000+01:00

Extending the PubChem and eMolecules gadgets, I created a simple interface for a basic ChemSpider interface for iGoogle.

I noticed a few issues and hope that those might get fixed

The InChiKey search on ChemSpider does not support searching only for the structure without stereochemical information, which would be just the first part of the InChIKey: structure-stereoinfo. So, I hope his could get fixed.
I tried finding more search options, e.g. defining substructure or exact searches, but could not find them in the ChemSpider tutorial. If there is more detailed information around I would appreciate getting some hints.

And just for clarity, here is a list of the other gadgets I have created

Boutique drugs: Splitting mass market and drug targets

2008-01-05T22:15:00.000+01:00

"Recently, the term boutique has started being applied to normally-mass-market items that are either niche or produced in intentionally small numbers at very high prices. For example, before the release of the Wii, a Time Magazine article suggested that Nintendo could become a boutique video-game company, producing games for niche audiences, rather than trying to compete directly with Microsoft and Sony [1]" [Boutique@WP]

"articles intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease in man or other animals and articles (other than food) intended to affect the structure or any function of the body of man or other animals." [FDA definition]

As found via Bio-IT/drug discovery expect analysts in the future more boutique drugs. Two main causes are mentioned.

First, the regulatory hurdles for getting a drug on the market have dramatically hardened since 2004. Furthermore has the number of black-box warnings (FDA guidance) at least doubled since that time.
Second, is there still a dwindling pipeline for new medications.

And I think Garnier and Brozak have the points when saying that it is difficult to react in a short time to many new drug projects/targets:

"The hurdle has been raised, there's no question about it! What is unclear is exactly how we should modify our drug development plans to meet this higher hurdle." [J. P. Garnier]
"No pharmaceutical executive has ever lost his job for saying no to a new drug project. And no FDA employee has ever lost his job for saying no to a new drug application." [S. Brozak]

Anyway, with a recent sad history for scientists it is to expect that drug design faces additional challenges in the next years, which are: changing drug design pradigms, avoiding paralysis.

I think it is possible, but I must say that some ideas are coming from people knowing the business, but not the science! On the other hand do "scientists, taken as a class, not always turn into the best managers" (via in the pipeline). Especially in this setup, changing seems difficult, because many are used to an established "blockbuster" drug design setup, which has worked for decades. Now, they have to define a way-out facing an innovaton funnel problem as well. If some of us scientists survive the next years, we can tell our grandchilds how some people and companies were able creating disruptive innovation helping patients in the upcoming decades.

References

Flat, round pills @WP
No smoking sign @WP

A community moves on, merry XMas and fantastic New Year 2008

2007-12-22T19:30:00.000+01:00

As posted by Deepak and Egon have several chem-/bio-informatics community members reached another milestone by publishing about userscripts in life sciences. In short, I am a believer in this technology. When reading, I got a little bit disappointed, because I got left with the feeling that I start loosing the touch to the community, though I try to follow and contribute as much as possible.

Loosing grip became also clear when I read the posts of Ola and Egon. I know that the community needs a self-selecting oligarchy for moving forward. You guys meet regularly in workshops and user group meetings. Some people might not have the time for this. In other words, you are part of the hard-core development team. This means, if you are not explaining the problems in more details and post more information somewhere, you might exclude some people, which do not have the time anymore digging trough the code themself.

As said earlier, I am working now in drug design with all the good and bad things about it (just read a few of Derek's posts and comments). This means my focus lies now on lead optimization, reading a lot, literature management, and data mining/graph mining, and only partially source code (if I can not call any support). This just means that I am very busy with many things, some of them I will and can not talk about, and this is good for my company community. As said before, data is not closed in general, but people have to accept some policies and confidentiality agreements. If you really want it, you have to talk to the right people and you need to have some very convincing arguments why you need this data.

While talking about policies, congratulations to Antony driving collaborations on several levels.

If there is something which helps people like me, for finding relevant information quickly, then that are two things:

userscripts, or in general social software, helping me finding information quickly
open standard databases, which allow people to contribute on several levels. Please note that I still think that open standards are more important than open access. 'Open' is for people in industry just a question of pricing, though I must say that some pricing strategies are just ridiculous and couterproductive for the work of scientists.

References

E. L. Willighagen, N. M. O'Boyle, H. Gopalakrishnan, D. Jiao, R. Guha, C. Steinbeck, D. J. Wild, Userscripts for the Life Sciences, Bioinformatics, 2007, 8, 487.
DOI 10.1186/1471-2105-8-487

Too many candles - World AIDS day 2007

2007-12-02T22:27:00.000+01:00

"Although treatments for AIDS and HIV exist to decelerate the virus' progression, there is currently no known cure." [AIDS@Wikipedia]

Help fighting AIDS !

source: amfAR

MetWare: metabolomics database

2007-11-25T18:11:00.000+01:00

via chem-bla-ics

The Applied Bioinformatics at PRI group in Wageningen and the group of Steffen Neumann in Halle have started the MetWare project on Sourceforge to develop opensource databases for metabolomics data.

The databases design will be based on and ideally compatible with proposed standards like ArMet (DOI:10.1038/nbt1041) and those recently written up by the Metabolomics Standards Initiative (see the issue around DOI:10.1007/s11306-007-0070-6).

One important design goal is that the project will use BioMart, which will allow easy integration of the database content in data analysis programs like Taverna and R using the biomaRt package (see DOI:10.1093/bioinformatics/bti525).

Once upon a cyclopentyl a cyclohexyl followed, and they lived not happily ever after ...

2007-11-08T23:14:00.000+01:00

via The Curious Wavefunction

One common strategy used by medicinal chemists for enhancing potency is to add a methylene carbon in a ring that's fitting into a lipophilic pocket. A methylene group usually contributes about 4 kJ/mol of affinity....or maybe not.

Gerhard Klebe's group at Marburg are studying inhibitors of thrombin in which they replace a cyclopentyl group by a cyclohexyl group. To their surprise they observe (DOI: 10.1002/anie.200701169) no change in binding affinity. Crystallography indicates no electron density for the part occupied by the cyclohexyl group but robust density for the cyclopentyl. The authors conclude that while the cyclohexyl binding is enthalpically unfavourable, entropically the six-membered ring can flip and twist and dance, which is favourable, and also does not provide much crystal density because of its flexibility. So unfavourable enthalpy is balanced by favourable entropy. MD simulations support this contention.
"What lesson can be learned from this example? Usually ligands are optimized in congeneric series. The addition of functional groups is expected to enhance binding; for example, the change from a five- to a six-membered ring should augment binding affinity by approximately 3–4 kJ/mol. However, even very similar ligands can exhibit very different binding properties that destroy a simple structure–activity relationship"
Also, the gain in binding affinity from a methylene depends on the system. Model systems based on octanol-water partitioning offer a value of about 0.7 kcal/mol. But of course octanol unlike a protein does not have a problem reorienting itself around the substrate. Thus, the value can be less for a protein. On the other hand, in cases where an extra methylene or methyl just about fills the remaining space in a pocket and packs tightly can provide 5 times as much binding affinity. This is true for example in case of tRNA synthatases (I got this from Alan Fersht's excellent book) As usual, exceptions abound to the generalisations, and Klebe's study provides another interesting example.

References

Cyclohexyl C1CCCCC1
Cyclopentyl C1CCCC1
C. Gerlach, M. Smolinski, H. Steuber, C. A. Sotriffer, A. Heine, D. G. Hangauer, G. Klebe, Thermodynamic Inhibition Profile of a Cyclopentyl and a Cyclohexyl Derivative towards Thrombin: The Same but for Different Reasons, Angewandte Chemie International Edition, 46,8511-8514, 2007. DOI 10.1002/anie.200701169

Andy Grove: When silicon indoctrinates carbon

2007-11-07T22:11:00.000+01:00

Andy Grove is right and we, the drug design community, can learn something from the chip industry. Derek, can you not see it, too? This strong relationship between those two families ...

"If you want to understand why something happens in business, study the disk drive industry. Those companies are the closest things to fruit flies that the business world will ever see. Drug design is a process between 9 to 15 years! So, which object to study lies in-between a fruit fly and a hard-disk? A high-throughput screening, a biological assay or an 'in silico' 3D/2D/xD model of a drug?" [Mining Drug Space]

Commenting on: Grim days for pharma

2007-11-03T02:35:00.000+01:00

via business|bytes|genes|molecules

Bloomberg says that drug sales are forecast to grow at their slowest ever rate in 44 years.

The WSJ reminds us that FDA approvals for NMEs are slow, and on a downward curve.

Perhaps it’s time for the pharma industry to really think about Pharma Futorology

Some of the consequences have been announced, and I think everybody hopes that those things will not happen every single year!

Can innovation help us for making drug design a mission possible? If yes, what are potential key factors for success?

Aligning scientists and goals

Putting scientists in the driver's seat
Goal congruence
Positive self-image
Fair evaluation system for high-performing scientist

Industral cultures that foster innovation

Take calculated risks
Openness and transparency
Adapt to constant changes
Facilitate effective collaboration

References

X. Zhong, G. B. Moseley, Mission possible: managing innovation in drug discovery, Nature Biotechnology, 2007, 25, 945-946. DOI 10.1038/nbt0807-945

Quotes from the neighbors (November 2007)

2007-11-02T23:07:00.000+01:00

==The value of feature extraction==

I am a strong believer in the power of machine readable data, and the potential power it can provide. Whether it be a system like Freebase that allows you to add structure to data, or the kinds of entity extraction and data contexts that Jon Udell and Jeff Jonas talk about, making your data more “intelligent” is something that we should strive for, whether it be in the world of business, or the world of life science (or as Andrew Walkingshaw might point out, materials science). My old colleagues at Scitegic have a motto - “Ask more of your data”. That has always resonated with me. So lets take yet another look at how to make your data smarter, especially when the semantic web seems to be inching towards more mainstream acceptance.

==Compiling the InChI Toolkit to Pure Java Bytecode with NestedVM==

Many cheminformatics libraries are written in C and C++; being able to reliably and automatically port them to Java could potentially save a great deal of effort.

==Come On. Improve, Already==

And man, have there ever been layoffs in the industry this year. There’s a list of the larger ones over at FierceBiotech, and it does not make for cheerful reading. In January, Pfizer announces 10,000 job cuts and closes their Ann Arbor site. That same month, Bayer-Schering closes the doors on research buildings in Connecticut and California (layoffs which were announced in 2006 and are thus not on the Fierce list). Bayer-Schering, who really should have run that B-S initial thing past a couple of native English speakers, announces 6,100 more job cuts in March. In July, AstraZeneca doubles down on its earlier layoff announcement and says that 7,600 jobs will disappear, and J&J announces a 4% reduction in its workforce (5,000 jobs). Then in August, Amgen cut over 2,000 jobs of its own.

Six Rules For Creating a Data Driven Drug Design Project

2007-11-02T19:14:00.001+01:00

"The problem is that we live in the most perfect imperfect medium in the world: the Web.
For now it is impossible to collect data perfectly. It is ugly, it is dirty, it is incomplete and no matter how hard you try it is not going to get perfect.
Yet we can’t resist." [Occam's razor]

Hahaha ... what a great statement of Avinash. Lets translate his statement about "Six Rules For Creating A Data Driven Boss!" into a drug design setting for getting "Six Rules For Creating a Data Driven Drug Design Project!".

# 1: Get Over Yourself

"In the past, many experimentalists shared a similar sentiment regarding of the usefulness of theoretical and computational methods to their drug discovery problems, namely: You're always explaining to me after the fact why a molecule i've already discovered is active, but you're never able to tell me ahead of time which molecules I should be investigating." [VanDrie]

If you have a better reasoning about drug design then random guessing then you should really get over yourself and give your opinion. You are working in a team, so appreciate challenging questions and feel free to challenge others as well. Otherwise, everybody can still hope for serendipity. But ...

"Hope is not a strategy" [H. R. Clinton]

# 2: Embrace Incompleteness

"I have heard that if the 'drug universe' included all the possible drug--like molecules, its size would be 10^62 molecules (other authors have proposed 10^200). Even if half of the currently known chemicals were drugs (a very optimistic assumption), this would produce 10^7 molecules. Therefore, at most, only 10^-53% of the possible drug--like molecules are known." [R. Lahana]

Just accept that we are working in an area where the chemical space is just too large for allowing us to get the complete picture of it. Thus, embrace incompleteness (bounded rational drug design).

Sometimes even rational ignorance might help a project, but you have to know how much effort it would be creating a complete data set.

# 3: Give 10% Extra
If you are asked to deliver results, then try to give 10% more then requested and show that you are rather a drug data ninja than a reporting squirrel. Information rules ... prepare yourself for answering questions and for having supporting information available! You have not to be a dictionary, but having 10% extra information is not too difficult.

"Make a conscious choice what job you want to be in: Reporting Squirrel or Analysis Ninja ?" [Occam's razor]

# 4: Become A Marketer

"If you want to change your boss and your company then you’ll have to become a Marketer, someone with an understanding marketing principles, someone who can be a customer advocate / champion, someone who can evangelize the purpose of data in creating customer centric decisions." [Occam's razor]

Everbody in drug design knows that 'real' data is the key for developing 'real' drugs. It costs many efforts and many people creating it. Drug design is not clicking buttons and counting bits and bytes. This is a big difference to web analytics. In drug design the data creation is an active process of the service provider, while in web analytics the data will be produced by any visitor to a web page (service provider).

Creating a new data point needs a team of passionated persons, so appreciate any single data point.

After the data is available, everybody in the team will be really excited to know if this data point will be the ultimative blockbuster.

Make clear, that data might help reducing risks. And even more important, do not sell things the data does not provide! Allow the data to influence a project, but avoid that overinterpreted data is misleading a project. Following marketing strategies might be one way of ranking data.

# 5: Business In The Service Of Data. Not.

"It is important, nay critical, to constantly check yourself and ask if the business is really serving the needs of data or vice a versa." [Occam's razor]

As said before is the data generation in drug design a time consuming and costly business. This means it is crucial to check if you can really learn from the data.

Ask yourself: Are we learning from the data?

If the answer is no, then something is wrong. Why are you producing data which costs time and money? Is the team not wasting their efforts?

Do not spend all your time in analyzing the data. Spend time in talking to the team for producing interesting data in the future.

Where do you want to go ? Go ahead! Avoid recursive data generation and conclusion cycles, which can keep you busy a while, but certainly not employed for the next years.

# 6: Adopt A New Mindset, Expand Horizons: Drug Design 2.0.
In drug design it does not count how many drugs you produce, it only counts how good they are.

Business intelligence (BI) and business performance management (BPM) can optimize the process. So what? If you are in a deadloop, it does not matter if you are in a normal or an optimized deadloop for data generation and data analysis? Get out of it!

Check what the competitors are doing! And if you are lucky, you can find this information in less than six steps ;-)

Resources

Thinking Man @Wikipedia
J. H. VanDrie, 3D Database Searching in Drug Discovery, Network Science, 1995. URL
Infinity @Wikipedia
Squirrel @Wikipedia

Uniprot: Over a billion protein database entries

2007-10-25T01:39:00.000+02:00

As posted by Eric Jain (via Egon) are now more than a billion RDF available.

The latest release of the UniProt protein database contains just over a
billion triples*! PRESS RELEASE :-)
The data is all available via the (Semantic or otherwise) Web:
http://beta.uniprot.org/
...or can be bulk-downloaded from:
ftp://ftp.uniprot.org/
* Counting some reification statements, and assuming no overlap between
"named graphs".
P.S. This should be the last you'll hear from me on this topic -- I'm off
to new adventures...

Furthermore is it notable that

The UniProt Knowledgebase (UniProtKB) and NCBI Reference Sequence (RefSeq) in conjunction with Entrez Gene are widely used resources for protein information, including function, classification and cross-references. Prior efforts in ID mapping (iProClass ID mapping table: http://pir.uniprot.org/search/idmapping) and recent collaborative efforts by UniProt and NCBI has resulted in the mapping of UniProtKB and RefSeq/Entrez Gene (GeneID) and incorporation of reciprocal cross-references in these databases. These cross-references will provide up-to-date links between the UniProt (http://www.uniprot.org) and NCBI databases (http://www.ncbi.nlm.nih.gov/RefSeq). Improving these mappings and increasing their coverage is an ongoing process.

Finally, is there a nice wink tutorial for diabetes around, which can be started here.

SuperTarget, Matador, ChemSpider: When data curation becomes a hype

2007-10-24T01:50:00.000+02:00

Although the first version of SuperTarget with all the searchand discovery tools around drug-target relations is alreadyan extensive resource for both large-scale research and in depthanalysis, the captured knowledge is still far from completeand we would like to invite the community to help in increasingquality and quantity of the records. SuperTarget offers an optionto upload and incorporate drug-target relations into a workingqueue. Uploaded entries will be reviewed and approved by anannotation team comprised of graduated scientists. Both SuperTargetand Matador can be used as knowledge sources, discovery toolsor training sets for various applications in chemical biologyand elsewhere. [DOI 10.1093/nar/gkm862]

Dear community, as already highlighted earlier is it a logical step that public data curation services like SuperTarget^, Matador, and ChemSpider become more popular. Lets see how far those services might go, but they should make things more efficient and help bringing light into the chaos. And yes, we are still living in a chaotic (data) world and every help counts.

For more access details for the databases contact the developers and authors. Here is some basic information.

SuperTarget and Matador are available via their web sites, http://insilico.charite.de/supertargetand http://matador.embl.de. They can be obtained via a CreativeCommons Attribution-Noncommercial-Share Alike 3.0 License. [DOI 10.1093/nar/gkm862]

You have limited rights in this regard. You can only assemble a database of 5000 structures or less, and their associated properties, from our database without our permission. You can download up to 1000 structures per day from the website. Please contact us at feedbackATchemspiderDOTcom to request an extension outside this constraint. We are willing to provide the ENTIRE database of ChemSpider structures at your request - the file will consist of InChI Strings, InChIKeys and ChemSpider IDs. These constraints are under regular review so please feel free to engage us in conversation. [ChemSpider FAQ]

References

Günther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, Schneider R, Skoblo R, Russell RB, Bourne PE, Bork P, Preissner R.
SuperTarget and Matador: resources for exploring drug-target relationships,
Nucleic Acids Res., 2007, ASAP alert.
DOI 10.1093/nar/gkm862
PMID 17942422

Open data, closed data, or the question about intellectual properties

2007-10-16T20:24:00.000+02:00

Now, this poses quite a challenge: different licenses, different copyright holders, requirements to provide access to the source (for the Open data), etc, all in one system. Quite a challenge indeed, because ChemSpider is now required to track copyright and license information for each bit of information. [chem-bla-ics]

... these techniques have traditionally been considered the realms of scientists from different disciplines, differences in computer systems and terminology provide a barrier to effective communication. This is probably the single most challenging problem that chemoinformatics must solve. [Hann/Green]

Do you know Open Data, Open Source, and Open Standards (ODOSOS)? I am one of the blue obelisk co-founders, but I think I have to take a slightly distant position now. I am not agreeing in all the recent statements made by single individuals, therefore can some statements not reflect the community opinion in a democratic sense as far as I have read the number of responses. All things said in this blog are my private opinion and do not reflect the opinion of any community I am related to or the opinion of the company I am working for.

Pro

Open to everybody: I think there is a clear positive effect of ODOSOS, but we have to be careful not to exaggerate the whole idea. I had never the intention to discourage people to join the community. The community must be open for everybody, and I seriously mean everybody! I do not care how much people can contribute to the community, but there should be the tendency that the whole community is making progress. And there are so many ways how to make progress ...
Open must not be free: I strongly believe that 'open', means not 'free' of charge. If people want to charge for services or products, they should be allowed to do that. If people want recognition, give it to them. If people want to give things away for free or without being named, please do this for accelerating progress or for being unknown. Academic partners might not have enough money to buy licenses, and I never liked this when being in academia. This might be critical, since many (but not all) scientific brains are sitting in academia. I am now in industry, so I can not be one of those scientific brains anymore (Sorry, Peter: 'This is very depressing' for you)! On the other hand; it is always possible providing cheap academic licenses and more costly industrial licenses. Two very nice examples are the OpenEye tools and PyMOL. I will not discuss the 'open source' topic in this area again, because Warren and Matt had already taken different positions in 2005 (which is a little bit outdated).
Increasing collaboration: One of the best examples being alive is Wikipedia, if you just look at the Alexa traffic ranking over the last years.
Increasing standards: As highlighted by Egon might the molecule mining world profit from different services when talking to each other and when working together. I personally think this is in-line with the two introductory quotes. This is the major challenge and it does not matter where people are coming from or for which company they are working for. If people can not work together then 'break down barriers' and create standards for working together! This is true for everybody, even for people which are not ODOSOS advocates. So lets play together in our community sandbox ...

Neutral

Patents: In contrast to Peter I believe that closed data and patents are crucial for any company. I see nothing bad in: 'Honor/money to whom is honor/money due'
A patent is a set of exclusive rights granted by a state to a patentee for a fixed period of time in exchange for a disclosure of an invention.
Again: 'open' must not be 'free', especially not because we are talking about a limited period. Maybe some people are in the luxurious position making idea gifts to others, some are not. If you think you can make those gifts then please send 800 million USD to a random 'open' bank account in the world. Following this video statement it is probably one in China or India and you should maybe go there, too. If you are then there, there is no risk for having an intellectual property shift. Though, shift might happen anyway

Contra

Poor verification: Peter has the habit to highlight the negative impacts of services and publishers as done with eMolecules/Wiley and Chemspider. As it turned out later were several of his points poorly verified, causing some confusions and tensions. Antony solved this very professionally by writing a very long counterstatement about the ChemSpider service. Please avoid unverified statements from both sides, the ODOSOS activists and the non-ODOSOS believers. I think there is some truth on both aspects. Please lets work together, not against each other.
Never use web-services and open source: Please be aware that some companies are not allowed in using web-services or open-source! You may ask why? Simply because nobody feels responsible for a secure data transfer. Just image an accident causes that an investment of 800 million USD gets lost, because another company has stolen your idea. If we all would be a happy open family nobody would care! Believe me, the last thing some people want is that everything becomes open. On the other hand are all people open to collaborate, if the security is guaranteed and the intellectual properties rights are clearly defined. So, dear ODOSOS activists, what are you doing to help answering those security questions and acknowledging the investments? Do not get me wrong, I am pro, but I am pro for both sides! I hope you are too!!!

Open source conformer generators are not a fairy tale

2007-10-14T00:55:00.000+02:00

FRee Online druG 3D conformation generator (Frog)
Noel announced that the source code will be incorporated into OpenBabel under the GPL.
smi23d - 3D Coordinate Generation
The source code is available under the Apache 2.0 license.

References

T. Bohme Leite, D. Gomes, M.A. Miteva, J. Chomilier, B.O. Villoutreix and P. Tufféry, Frog: a FRee Online druG 3D conformation generator, Nucl. Acids Res., 2007, 35, W568-W572. DOI 10.1093/nar/gkm289
D. K. Agrafiotis, H. Xu, A self-organizing principle for learning nonlinear manifolds, Proc. Nat. Acad. Sci., 2002, 99, 15869-15872. DOI 10.1073/pnas.242424399
S. Izrailev, F. Zhu, D. K. Agrafiotis, A distance geometry heuristic for expanding the range of geometries sampled during conformational search, J. Comp. Chem., 2006, 27, 1962-1969. DOI 10.1002/jcc.20506

Finding a job in cheminformatics and modeling?

2007-10-11T21:29:00.000+02:00

From: jobs at ccl.net (do not send your application there!!!)
To: jobs at ccl.net
Date: Mon Sep 3 07:45:15 2007
Subject: 07.09.03 Senior/Principal Scientist Molecular Modeling, Tibotec, Belgium

Tibotec (http://www.tibotec.com) is a pharmaceutical research and development 
company with headquarters in Belgium and operating subsidiaries in the 
United States and Ireland. The company is a subsidiary of Johnson & 
Johnson. Tibotec is dedicated to the discovery and development of novel 
drugs for HIV/AIDS and other infectious diseases with the ultimate aim 
of enhancing and extending peoples lives.

We currently are looking for a Senior or Principal Scientist Molecular 
Modeling and Cheminformatics, based at our headquarters in Mechelen, 
Belgium.
In this function you will apply computational chemistry techniques 
to the discovery and optimization of small molecule drug candidates. 
You will be an important member of one or more drug discovery project 
teams and interact closely with chemists and biologists to drive the 
identification and optimization of drug candidates. As a member of the 
Computational Virology and Chem/Bio-Informatics group you will also play 
an active role in the development and implementation of the strategy 
of the group.

Qualifications and Requirements 
* Ph.D. degree in Computational Chemistry or related field
* 5+ years of industrial experience in computational chemistry
* Experience with a wide range of computational techniques, 
  including structure based and non-structure based methods
* Expertise in scientific programming and/or scripting
* Outstanding communication and team work skills.

For more information and to apply please contact hvvlijme#tibbeDOTjnjD0Tcom


Other job offers

Quotes from the neighbors (October 2007)

2007-10-10T22:39:00.000+02:00

==Taverna Workshop, Day 1 Update (chem-bla-ics)==

The second part of the morning session featured a presentation by Sirisha Gollapudi which spoke about mining biological graphs, such as protein-protein interaction networks and metabolic pathways. Patterns detection for nodes with only one edge, and cycles etc, using Taverna. An example data she worked on is the Palsson human metabolism (doi:10.1073/pnas.0610772104); she mentioned that this metabolite data set contains cocaine :) Neil Chue Hong finished with an introduction on the OMII-UK which is co-host of this meeting.

After lunch Mark Wilkinson introduced BioMoby, which we actually use in Wageningen already. I have tried to use jMoby to set up services based on the CDK, but failed sofar. Will talk with Mark on that. Next was my presentation, and I spoke about CDK-Taverna, Bioclipse and some peculiarities with chemoinformatics workflow, like the importance with intermediate interaction, the need to visualize the data and complex, information rich data. Bioclipse is seeing an integration of BioMoby and of Taverna.

After the coffee brake Marco Roos spoke about myExperiment and his work on text mining. I unfortunately missed this presentation, as I was meeting with people from the EBI who work on the MACiE database (see this blog item).

==The chemical blogosphere cares (petermr's blog)==

Wow! I posted a request yesterday (sic) for supporting material for our proposal to JISC for a person to support the blogosphere as a major resource for increasing the quality of published chemistry. I have had valuable contributions from 4 people already and now Egon has created a wonderful summary - just the right length. We’ll either include it as it stands of point to it from the proposal - depends on space. [Recall that Egon’s Chemical Blogspace blog aggregates the whole of chemical blogspace. ]... and here is Egons post: How the blogosphere changes publishing

==Helping the Cheminformatics Community Through Our Web Services (ChemSpider Blog)==

Rich Apodaca is using our services (ChemSpider) and, being Rich (and I mean that in a GOOD way!) he is being more demanding. ...
Rich> Me… demanding? What ever gave you that idea?
Rich> Actually, this is great stuff.
Rich> But…

==Eigenfactor: Ranking and mapping scientific knowledge (bbgm)==

Yesterday, I proposed that Google Scholar would benefit from a Plus Box. In that post I alluded to Eigenfactor, a wonderful way of evaluating the network of scientific information, with publishing at its heart.

I had the pleasure of meeting Carl Bergtrsom from the University of Washington at Scifoo, where he showcased Eigenfactor. I remember sitting there, my head churning, admiring how wonderfully his group had implemented it, and the potential the service had for scientific publishing and finding scientific information.

==SMILES to become an Open Standard (chem-bla-ics)==

Craig James (eMolecules) wants to make SMILES an open standard, and this has been received with much enthusiasm. SMILES (Simplified molecular input line entry specification) is a de facto standard in chemoinformatics, but the specification is not overly clear, which Craig wants to address. The draft is CC-licensed and will be discussed on the new Blue Obelisk blueobelisk-smiles mailing list.

Illustrative is my confusion about the sp2 hybridized atoms, which use lower case element symbols in SMILES. Very often this is seen as indicating aromaticity. I have written up the arguments supporting both views in the CDK wiki. I held the position that lower case elements indicated sp2 hybridization, and the CDK SMILES parser was converted accordingly some years ago. A recent discussion, however, stirred up the discussion once more (which led to the aforementioned wiki page).

Six sigma in drug design - Old hands or fresh blood?

2007-09-15T23:54:00.000+02:00

K. Ramakrishnan asked the question

Will implementation of Six sigma, TQM, ISO improve efficiency or hinder the innovation?
What would be an ideal process or methodology to improve innovation in drug discovery?
What would be the best methodology or way to build an integrated team with good communication and coordination to accelerate both innovation and efficiency in drug discovery?

Might there be a connection to the six degrees of drug design? And what helps more? Old hands or fresh blood? Are people here just playing with buzz-words or can anybody really give some more information about those topics?

Pathguide - Light in the pathway and interaction jungle?

2007-09-15T15:50:00.000+02:00

I found today in my del.ico.us network a bookmark from Egon to Pathguide

Pathguide contains information about 231 biological pathway resources. Click on a link to go to the resource home page or 'Details' for a description page. Databases that are free and those supporting BioPAX, CellML, PSI-MI or SBML standards are respectively indicated.

This is a fantastic collection of interaction/metabolic/signaling pathways and networks!
Though, I was astonished and a little bit disappointed about the low number of databases, which support open standards.

14 PSI-MI (Proteomics Standards Initiative - Molecular Interactions)
13 BioPAX (Biological Pathways Exchange)
13 SBML (Systems Biology Markup Language)
3 CellML
---------
43 databases use open standards

That are only 18.6% of all databases! In other words, this confirms again the collaboration problem. Are people really thinking that it already helps to put things online or sharing it as open source? This is just not true as already summarized before and especially not if an oligarchy has to create those standards.

Open source tournament - RDKit enters the arena

2007-09-14T20:40:00.000+02:00

Egon is right! It is great that more and more open source tools are entering the drug design arena. On the other hand, we really need some benchmarking and feature comparisons for allowing to learn which tools should be used for which tasks.

Noel dashed off an email to Greg Landrum, the main developer (who it turns out is also the developer of YAeHMOP (Yet Another extended Huckel Molecular Orbital Package) ), and he asked him what the story was. Two days ago, he returned from holidays and pointed Noel to the correct website and the documentation, and Noel couldn't believe what he was seeing...

Some features that are cool:
(1) Molecules based on the Boost Graph Library
(2) All the Python stuff works for me on Windows!
(3) 2D depiction!!!
(4) 2D depiction that mimics 3D conformations!!!
(5) 2D --> 3D conversion in a similar method to Rajarshi's smi23D! (doesn't use stochastic promixity embedding though)

Here's a summary of some of the rest: SMILES, substructure searching, sophisticated fingerprints, machine learning stuff, a GUI, clustering, MACCS keys, descriptors (84 or so), chemical reaction transformations, implementation of Recap (not sure what this is, but there's a ref in the docs), basic pharmacophore stuff, and two types of SSSR (there's some text about this in the docs).

Quotes from the neighbors

2007-09-13T00:30:00.000+02:00

==DrugBank continues to offer open access drug information==

The DrugBank database is a freely available, bioinformatics and cheminformatics resource that combines chemical, pharmacological and pharmaceutical drug data with comprehensive drug target (i.e. protein) information. [Chemistry Central]

==Zinc mediated inhibitors==

At pH 8.2 the inhibition constant (Ki) of BABIM for bovine trypsin in 1.0 mM EDTA, pH 8.2, is 19 uM. Addition of Zn²+ at concentration as low as 100 nM increases the affinity of BABIM for trypsin by 3,800 to Ki = 5.0 ± 0.3 nM. Zn²+ alone is a weak inhibitor of trypsin with a Ki of ~ 33 mM at pH 5.5, and ~ 1.0 mM at pH 7.0. [whistling in the wind]

==Why InChIKey?==

... the only problem with InChI is the length of the identifiers and the issues caused by the characters used. This could be solved by having a centralized service that assigned short HTTP URLs for InChIs, ensuring a one to one relationship between InChIs and shorthand URLs. [Jim Downing]

Sounds like TinyURL to me.

Social chemistry and Wikipedia

2007-09-12T23:05:00.000+02:00

As posted already are there still several problems with structure/substructure searches, and even the plain indexing of chemical structures within Wikipedia. Here are more details about it.

==One==
1.1. There are technical problems, because a SMILES code might contain characters reserved for the Wikipedia syntax.
1.2. Some people dislike SMILES strings for large molecules
1.3. It seems not to be easy to convince people about the relevance of unified chemical identifiers within Wikipedia, and subtructure and structure search link-outs. Especially the Wikipedia administration seems to be very skeptical and non-responsive.

Substructure search in eMolecules and PubChem added

I have added a SMILES based substructure search for eMolecules and PubChem. Please have a look at Cetirizine containing already SMILES code and this can been seen as an example.

Please comment on possible improvements. Things I considered

How to improve visualization for SMILES? Smaller font, truncating it?
Instead of search in should just use images for eMolecules and PubChem?
Undisplaying SMILES, but keeping the external substructure search option.

JKW 13:47, 27 January 2007 (UTC)

Hi there. Sorry to raise an objection, but wouldn't this clutter the template much? SMILES is rarely used as is, and wouldn't it really look bad on, say, large molecules such as paclitaxel or vancomycin :)? Fvasconcellos 14:10, 27 January 2007 (UTC)
Hi, the problem is bigger, the code breaks when there is a "]" or something like that in the SMILES, what occurs in some cases. I'd be interested in a solution, though. Just as a note, there is a discussion about moving all the 'chemboxes' to the new chembox new on the chemistry wikiproject, you might want to join that discussion. --Dirk Beetstra ^{T C} 14:14, 27 January 2007 (UTC)
I agree on both. First, SMILES is an option not a must and a workaround might be just having the link-out for the substructure search, but not displaying the SMILES for all entries. Second, breaking the Wiki-Code is a serious issue and can we not just add SMILES as raw data by using nowiki or pre tags? And in general a substructure search contains a lot of information, so I would like to keep it somehow. The same is also the case for InChI since this is indexed by Google as well. JKW 14:36, 27 January 2007 (UTC)
I tried a nowiki tag without success, because this blocked the template replacements. I need help here. The SMILES code was excluded, but the search in eMolecules and PubChem was kept. JKW 14:55, 27 January 2007 (UTC)
I have tried quite some things, but all seem to fail, or to just give an improperly encoded SMILES/InChI. I could code this for you (e.g. ) which would create a proper link (I have done something like that for chemical formulae, see [1], chemistry part of that test-wiki). Might have a look tomorrow. Problem is going to be to convince Brion and Tim to enable these tags on en.wikipedia.org. See you around. --Dirk Beetstra ^{T C} 15:03, 27 January 2007 (UTC)

==Two==
2.1. Beside the already mentioned issues causes the drug box template problems for unformatted SMILES code. A very recent example from last week is the Dapagliflozin article, an experimental SGLT2 inhibitor.
2.2. I stumbled last week over the 'corrupted' drug box in this article

2.3. I checked the syntax and figured out that a completely valid SMILES code was used. Unfortunately is this not breaking automatically, so I reverted back to a previous revision, in which the only change was the removal of the HTML line-breaks. This means we have a problem here. Either we keep a valid and unfromatted SMILES entry or we make the drug box unreadable.

==Three==
3.1. The drug box template and the chem box new template are really different and some people would like to get a unified solution for avoiding redundancy and lacking standards. I am, of course, one of them!
3.2. In fact, the situation is even worse, because we have multiple boxes and templates out there, which are increasing diversity. Some further things used are explosive box template, and some deprecated solutions like in the (old) Anthrone article, which was fixed yesterday by Rifleman82. Now also the chembox new is used. Nonetheless are there some (deprecated!?) templates around, which might be used in other articles, e.g. the InChI link template.
3.3. Beside all those organisational problems, there is also the question if Wikipedia parser functions (see also MediaWiki help) should be used, and to which degree they should be used? I think that we do not have many choices, because properly encoded molecules might encode characters, which might cause problems for several world-wide-web applications. Believe me, I know that molecular query languages are not easy for several reasons. Anyway, as also highlighted by Rich is this not an easy problem. Sorry Rich, I really dislike the WLN. In other words, I clearly support parser functions, because they can break-down syntax barriers when encoding chemistry.

I hope this gives some further insights and again, please comment, blog, distribute, or support! Anything that helps making some steps forward is highly appreciated.

Wikipedia, DBPedia, and the remaining problems with chemical searches

2007-09-11T20:26:00.000+02:00

At the beginning there was ... chaos

I think many people know that I am a big fan of structured data mining. As highlighted by Noel and Peter are Wikis one way for collecting this kind of information on a global level. Wikis are also nice, because even the data structures (just part of the Wiki) might evolve.
This idea is not new, but chemical data is still not there, yet! Yesterday, I was raising (again) the question what would be the best way to extend Wikipedia with structure searches? I was one of those establishing a first attempt enabling PubChem and eMolecules searches. I think you will agree that this is not general enough, because there are more databases out there. Especially, ChemSpider (@Wikipedia) is riding the web 2.0 wave very well, and it would be nice to see that even more sources are joining.

I must say that I had several eMail discussions behind the scenes and believe me, sometimes I hate it that nobody feels responsible in communities, but everybody has an opinion. In other words, I need more support

Dear cheminformatics community and friends of structure searches within Wikipedia,

please help to reach the tipping point and support this, yes this!, request by adding comments or offering support!

1. I would like to get unique identifiers within Wikipedia, which are not breaking the drug box, chem box, or intermediate solutions using Wikipedia templates. I also do not like unreadable lengthy SMILES or InChI on Wikipedia pages, they do not really add value, because most people will not use them for large molecules.

2. I would like to get multiple search services, not only a few. This is crucial for creating knowledge.

3. I would like to see that structure identifiers are getting indexed by Google, which is impossible for reformatted SMILES and InChIs on Wikipedia. On the other hand is it impossible keeping this information in one piece in the boxes, because it screws them up.

4. I would like to get a consens and more publicity for getting the Wikipedia admins making step forwards, e.g. by supporting a chemical special search site. Otherwise we need a very robust and trustworthy supplier offering this service. Can the IUPAC help here?

5. I would like to see intelligent parser functions overcoming formatting problems of structural information. I think they are robust enough, and they are not that bad. So, please give the chemical and drug boxes free again for editing!

Please comment, blog, or do anything else, which helps getting this message to the right people! Especially this part is tricky on Wikipedia, since there might not be really 'one' person in charge.

Cheers, Joerg

Only if we get unified Wikipedia entries for molecular structures and proper link-outs to all relevant pages I see a chance that the community can benefit from the massive collaboration efforts.

Wikipedia hits two million articles - Not bad for an oligarchy

2007-09-09T21:48:00.000+02:00

The number of articles in Wikipedia has reached 2,000,000 . I think this is not bad for a self-selecting oligarchy. So, thanks to the top 2-10% users of Wikipedia, may the Web 2.0 with you ;-)

BTW, I was informed by Derek Cashman that there was an initiative started. The WikiProject is called Pharmacology/Collaboration of the Week. Feel free to watch the status or to contribute!

The Law of the Few contends that before widespread popularity can be attained, a few key types of people must champion an idea, concept, or product before it can reach the tipping point. Gladwell describes these key types as Connectors, Mavens, and Salesmen. If individuals representing all three of these groups endorse and advocate a new idea, it is much more likely that it will tip into exponential success. [The Tipping Point (summary)]

References

Wikipedia, 2 million pool
Oligarchy@Wikipedia

Mining Drug Space

Top 10 misconceptions in science communication

Should scientists care about a Wiki for Knowledge Management?

Life Science in 2020 - Vision of students joining ESOF 2010

It's not filter failure. It's a discovery deficit.

Science plog'ing, S3'ing, and other trends

Molecular design and molecular modeling basics

Friend of a friend - How to create a FOAF science network?

Social media needs to support different scientific personalities

Sky-rocking graphics in new MOE release

Warren DeLano - In Memoriam

Innovation 2.0 - Looking for a strategy in drug design and health care?

50 million chemicals, and accelerating - chemical patent overload?

The Web for Patients, Pets, and Scientists (Panel Discussion)

Google Wave - Mission Drug Design Federation

Drug popularity (via Google queries) - Yet Another Long Tail (YALT)

Compliant commenting in science 2.0

Peer recognition and building relationships - Even in drug design

Tag cloud - Mining Drug Space - May 2009

Drug design and thinking the unthinkable

Open innovation in drug design? - I do not think so!

The future of science and scientists - walking into open innovation ?

Bridging chemistry islands to the web data silo

Do drug development (D3) - Innovation while information ignorance is increasing?

Reflect - annotate protein and small molecule names in web pages

Drug design paradigms - get involved in helping patients - haystacks, islands, and cliffs

Nature - mission science - join the blogosphere

Chemical suppliers - CHEMCATS and the long tail

Life science - Are the IT guys getting it ?

Collaboration bullseye 2.0: Information overload, filter failure, and ways out

Antithrombotic graph visualization - Chemistry 2.0 and the ChemSpider journal

Drug designers are creating innovative ideas

Group Contribution revisited (via Fragment Based Drug Design)

Mining clinical trials

Public data sets on AWS (Amazon Web Services)

Python Training for Cheminformatics and Modeling

Support the star of life 2.0

Data, models, or both?

Quotes from the neighbors (July 2008)

WikiPathways - the challenge of community curation

OpenAstexViewer - an open structural biology viewer

Science - editorial, social, or both

WikiProteins - May the community be with you

Lasso, docking, and protonation

CAS numbers are not public domain, are they?

Yet Another Life Science Informatics (YA-LSI) networking portal

Social search in drug design and what is Wikia

Pipeline pilot: Student edition

Chemical webs, nets, and spiders

ChemSpider search on iGoogle

Boutique drugs: Splitting mass market and drug targets

A community moves on, merry XMas and fantastic New Year 2008

Too many candles - World AIDS day 2007

MetWare: metabolomics database

Once upon a cyclopentyl a cyclohexyl followed, and they lived not happily ever after ...

Andy Grove: When silicon indoctrinates carbon

Commenting on: Grim days for pharma

Quotes from the neighbors (November 2007)

Six Rules For Creating a Data Driven Drug Design Project

Uniprot: Over a billion protein database entries

SuperTarget, Matador, ChemSpider: When data curation becomes a hype

Open data, closed data, or the question about intellectual properties

Open source conformer generators are not a fairy tale

Finding a job in cheminformatics and modeling?

Quotes from the neighbors (October 2007)

Six sigma in drug design - Old hands or fresh blood?

Pathguide - Light in the pathway and interaction jungle?

Open source tournament - RDKit enters the arena

Quotes from the neighbors

Social chemistry and Wikipedia

Substructure search in eMolecules and PubChem added

Wikipedia, DBPedia, and the remaining problems with chemical searches

Wikipedia hits two million articles - Not bad for an oligarchy