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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.
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Cited by
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Self-citationWalters, W. P., Stahl, M. T., & Murcko, M. A. (1998) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01163-x · Cited by 991
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Do Structurally Similar Molecules Have Similar Biological Activity?
Martin, Y. C., Kofron, J. L., & Traphagen, L. M. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020155c · Cited by 806
Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698
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Self-citationComparison of Topological, Shape, and Docking Methods in Virtual Screening
McGaughey, G. B., Sheridan, R. P., Bayly, C. I., Culberson, J. C., Kreatsoulas, C., Lindsley, S., Maiorov, V., Truchon, J.-F., & Cornell, W. D. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700052x · Cited by 417
Self-citationWhy do we need so many chemical similarity search methods?
Sheridan, R. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02411-x · Cited by 412
Self-citationTime-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376
Self-citationIs Multitask Deep Learning Practical for Pharma?
Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00146 · Cited by 263
Self-citationDemystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships
Xu, Y., Ma, J., Liaw, A., Sheridan, R. P., & Svetnik, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00087 · Cited by 238
Self-citationSheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84
Self-citation
Metadata
- Authors
- Kearsley, S. K., Sallamack, S., Fluder, E. M., Andose, J. D., Mosley, R. T., & Sheridan, R. P.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1996-01-01
- Volume
- 36
- Issue
- 1
- Pages
- 118-127
- ISSN
- 0095-2338 (ISSN-L)
- 1520-5142
- OpenAlex
- W2026910177
- Cited by
- 267
- References
- 0
- FWCI
- 20.61
- Type
- journal article
- Language
- en
- Open access
- closed
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