Chemical Similarity Using Physiochemical Property Descriptors

Abstract

Similarity searches using topological descriptors have proved extremely useful in aiding large-scale screening. We describe alternative forms of the atom pair (Carhart et al. J. Chem. Inf. Comput. Sci. 1985, 25, 64-73.) and topological torsion (Nilakantan et al. J. Chem. Inf. Comput. Sci. 1987, 27, 82-85.) descriptors that use physiochemical atom types. These types are based on binding property class, atomic log P contribution, and partial atomic charges. The new descriptors are meant to be more “fuzzy” than the original descriptors. We propose objective criteria for determining how effective one descriptor is versus another in selecting active compounds from large databases. Using these criteria, we run similarity searches over the Derwent Standard Drug File with ten typical druglike probes. The new descriptors are not as good as the original descriptors in selecting actives if one considers the average over all probes, but the new descriptors do better for several individual probes. Generally we find that whether one descriptor does better than another varies from probe to probe in a way almost impossible to predict a priori. Most importantly, we find that different descriptors typically select very different sets of actives. Thus it is advantageous to run similarity probes with several types of descriptors.

DOI: 10.1021/ci950274j

9 Figures and Tables

0102030'99'01'03'05'07'09'11'13'15'17
Citations per Year

186 Citations

Semantic Scholar estimates that this publication has 186 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Kearsley1996ChemicalSU, title={Chemical Similarity Using Physiochemical Property Descriptors}, author={Simon K. Kearsley and Susan Sallamack and Eugene M. Fluder and Joseph D. Andose and Ralph T. Mosley and Robert P. Sheridan}, journal={Journal of Chemical Information and Computer Sciences}, year={1996}, volume={36}, pages={118-127} }