The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds
Abstract
The computer system PASS provides simultaneous prediction of several hundreds of biological activity types for any drug-like compound. The prediction is based on the analysis of structure-activity relationships of the training set including more than 30000 known biologically active compounds. In this paper we investigate the influence on the accuracy of predicting the types of activity with PASS by (a) reduction of the number of structures in the training set and (b) reduction of the number of known activities in the training set. The compounds from the MDDR database are used to create heterogeneous training and evaluation sets. We demonstrate that predictions are robust despite the exclusion of up to 60% of information.
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Brown, R. D., & Martin, Y. C. (1996) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9501047 · Cited by 625
Similarity and clustering in chemical information systems
(1987) · European Journal of Medicinal Chemistry · doi:10.1016/0223-5234(87)90020-1 · Cited by 446
Filimonov, D., Poroikov, V., Borodina, Y., & Gloriozova, T. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980335o · Cited by 198
Self-citationQuantitative structure-activity relationships and the unnamed science
Hansch, C. (1993) · Accounts of Chemical Research · doi:10.1021/ar00028a003 · Cited by 172
Apostolakis, J., & Caflisch, A. (1999) · PubMed · Cited by 34
Cited by
Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., & Poroikov, V. V. (2014) · Chemistry of Heterocyclic Compounds · doi:10.1007/s10593-014-1496-1 · Cited by 1,346
Self-citationMachine learning in chemoinformatics and drug discovery
Lo, Y.-C., Rensi, S. E., Torng, W., & Altman, R. B. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.05.010 · Cited by 960
In silico toxicology: computational methods for the prediction of chemical toxicity
Raies, A. B., & Bajic, V. B. (2016) · WIREs Computational Molecular Science · doi:10.1002/wcms.1240 · Cited by 744
QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction
Lagunin, A., Zakharov, A., Filimonov, D., & Poroikov, V. (2011) · Molecular Informatics · doi:10.1002/minf.201000151 · Cited by 379
Self-citationCERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510267 · Cited by 375
Metadata
- Authors
- Poroikov, V. V., Filimonov, D. A., Borodina, Y. V., Lagunin, A. A., & Kos, A.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2000-09-30
- Volume
- 40
- Issue
- 6
- Pages
- 1349-1355
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2039369430
- Cited by
- 254
- References
- 5
- FWCI
- 9.00
- Type
- journal article
- Language
- en
- Open access
- closed
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