Conformational Boosting

Dimitris K. Agrafiotis; Alan Gibbs; Fangqiang Zhu; Sergei Izrailev; Eric Martin

doi:10.1071/CH06217

RESEARCH FRONT

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Conformational Boosting

Dimitris K. Agrafiotis ^A ^D , Alan Gibbs ^A , Fangqiang Zhu ^A , Sergei Izrailev ^A ^B and Eric Martin ^C

+ Author Affiliations

- Author Affiliations

^A Johnson & Johnson Pharmaceutical Research & Development, 665 Stockton Drive, Exton, PA 19341, USA.

^B Current address: Fortress Investment Group, 1345 Avenue of the Americas, New York, NY 10105, USA.

^C Chiron Corporation, 4560 Horton Street, Emeryville, CA 94608, USA.

^D Corresponding author. Email: dagrafio@prdus.jnj.com

Australian Journal of Chemistry 59(12) 874-878 https://doi.org/10.1071/CH06217
Submitted: 21 June 2006 Accepted: 21 October 2006 Published: 20 December 2006

Abstract

Stochastic proximity embedding (SPE) is a novel self-organizing algorithm for sampling conformational space using geometric constraints derived from the molecular connectivity table. Here, we describe a simple heuristic that can be used in conjunction with SPE to bias the conformational search towards more extended or compact conformations, and thus greatly expand the range of geometries sampled during the search. The method uses a boosting strategy to generate a series of conformations, each of which is at least as extended (or compact) as the previous one. The approach is compared to several popular conformational sampling techniques using a reference set of 59 bioactive ligands extracted from the Protein Data Bank, and is shown to be significantly more effective in sampling the full range of molecular radii, with the exception of the Catalyst program, which was equally effective.

Acknowledgments

We thank Dr David J. Diller of Pharmacopeia, Inc. for providing the dataset and Dr Huafeng Xu of D. E. Shaw & Co. for his earlier work on SPE and for his critical review of this manuscript.

References

Conformational Boosting

Abstract

Acknowledgments

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