On the effects of permuted input on conformational sampling of drug-like molecules: an evaluation of stochastic proximity embedding.

ABSTRACT

Conformational sampling is a problem of central importance in computer-aided drug design. A good conformational search method must not exhibit any intrinsic bias, and must provide confidence that important regions of conformational space are not missed during the search. A recent study by Carta et al. showed that this is not always the case, and that several popular conformational search methods, such as Omega, are very sensitive to the relative ordering of atoms and bonds in the connection table. Here, we examine the performance of a newer method known as stochastic proximity embedding, or SPE, using five diverse bioactive ligands extracted from the PDB. Our results confirm that the conformational ensembles produced by SPE using different permuted inputs are statistically indistinguishable, and well within the range of variability that would be expected from the stochastic nature of the method itself. This, along with the results of a more comprehensive comparative study (Agrafiotis et al., J. Chem. Info. Model, 2007, in press), provides further evidence that SPE is one of the most robust and competitive conformational search methods described to date.