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Chemical Similarity Using Geometric Atom Pair Descriptors
Abstract
Similarity searches using topological descriptors have proved extremely useful in aiding large-scale screening. In this paper we describe the geometric atom pair, the 3D analog of the topological atom pair descriptor (Carhart et al. J . Chem . Inf . Comput . Sci . 1985, 25, 64−73). We show the results of geometric similarity searches using the CONCORD-build structures of typical small druglike molecules as probes. The database to be searched is a 3D version of the Derwent Standard Drug File that contains an average of 10 explicit conformations per compound. Using objective criteria for determining how good a descriptor is in selecting active compounds from large databases, we compare the results using the geometric versus the topological atom pair. We find that geometric and topological atom pairs are about equally effective in selecting active compounds from large databases. How the two types of descriptors rank active compounds is generally about the same as well, but occasionally active compounds will be seen as very similar to a probe in geometric descriptors, but as fairly dissimilar in topological descriptors. These are of two types: (1) compounds where equivalent groups are in the same spatial arrangement as in the probe but are connected by very different bond paths and (2) compounds that can superimpose onto the probe when they are in a folded conformation.
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Cited by
Willett, P., Barnard, J. M., & Downs, G. M. (1998) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9800211 · Cited by 1,765
Similarity-based virtual screening using 2D fingerprints
Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849
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
Frequent substructure-based approaches for classifying chemical compounds
Deshpande, M., Kuramochi, M., Wale, N., & Karypis, G. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.127 · Cited by 371
Zamberlan, F., Sanz, C., Martínez Vivot, R., Pallavicini, C., Erowid, F., Erowid, E., & Tagliazucchi, E. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00054 · Cited by 71
Metadata
- Authors
- Sheridan, R. P., Miller, M. D., Underwood, D. J., & Kearsley, S. K.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1996-01-01
- Volume
- 36
- Issue
- 1
- Pages
- 128-136
- ISSN
- 0095-2338 (ISSN-L)
- 1520-5142
- OpenAlex
- W2026909852
- Cited by
- 183
- References
- 0
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sheridan, R. P., Miller, M. D., Underwood, D. J., & Kearsley, S. K. (1996). Chemical Similarity Using Geometric Atom Pair Descriptors. Journal of Chemical Information and Computer Sciences. https://doi.org/10.1021/ci950275b
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