Conformational Boosting

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.