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
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6 comments:
Two points: smi23d no longer includes SPE (due to patent problems), and also, RDKit has a similar algorithm to smi23d.
Thanks for letting me know, I was not aware of this.
Can you please forward me to the literature references for the actual used 3D generation or is there no publication for it?
Anyway, is the
RDKit algorithm documented with more details?
At this point there's no publication for smi23d - but Noel is correct in pointing out that it does not use SPE anymore
Greg Landrum of RDKit said: "We do *not* use SPE or anything related to it. Our approach for embedding the coordinates from the distance matrix is to just use the standard diagonalization procedure that is used by things like DGeom. SPE (and related algorithms) is really overkill for small molecules."
SPE is an overkill?! Have you actually read the algorithm? How much simpler does it get than taking pairs of atoms and adjusting their coordinates along their axis? It is the simplest and fastest algorithm available. I would also recommend the following paper that demonstrates its superiority in terms of sampling:
D. K. Agrafiotis, A. Gibbs, F. Zhu, S. Izrailev, and E. Martin, "Conformational sampling of bioactive molecules: a comparative study", J. Chem. Info. Model., 2007, 47, 1067-1086.
There are a lot more such papers coming. And who knows, maybe the software as well.
The DOI of the JCIM 2007 article is 10.1021/ci6005454
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