The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k-Nearest-Neighbor Principle
Abstract
A novel automated variable selection quantitative structure-activity relationship (QSAR) method, based on the kappa-nearest neighbor principle (kNN-QSAR) has been developed. The kNN-QSAR method explores formally the active analogue approach, which implies that similar compounds display similar profiles of pharmacological activities. The activity of each compound is predicted as the average activity of K most chemically similar compounds from the data set. The robustness of a QSAR model is characterized by the value of cross-validated R2 (q2) using the leave-one-out cross-validation method. The chemical structures are characterized by multiple topological descriptors such as molecular connectivity indices or atom pairs. The chemical similarity is evaluated by Euclidean distances between compounds in multidimensional descriptor space, and the optimal subset of descriptors is selected using simulated annealing as a stochastic optimization algorithm. The application of the kNN-QSAR method to 58 estrogen receptor ligands as well as to several other groups of pharmacologically active compounds yielded QSAR models with q2 values of 0.6 or higher. Due to its relative simplicity, high degree of automation, nonlinear nature, and computational efficiency, this method could be applied routinely to a large variety of experimental data sets.
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Cites
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Cited by
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Self-citationRational selection of training and test sets for the development of validated QSAR models
Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1025386326946 · Cited by 712
Self-citationMachine learning methods in chemoinformatics
Mitchell, J. B. O. (2014) · WIREs Computational Molecular Science · doi:10.1002/wcms.1183 · Cited by 469
Big data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455
Quantitative Nanostructure−Activity Relationship Modeling
Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S. Y., Mumper, R. J., & Tropsha, A. (2010) · ACS Nano · doi:10.1021/nn1013484 · Cited by 388
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Metadata
- Authors
- Zheng, W., & Tropsha, A.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1999-11-19
- Volume
- 40
- Issue
- 1
- Pages
- 185-194
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2002503643
- Cited by
- 450
- References
- 56
- FWCI
- 9.53
- Type
- journal article
- Language
- en
- Open access
- closed
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