title: QSAR using evolved neural networks for the inhibition of mutant PfDHFR by pyrimethamine derivatives.
abstract: Quantitative structure-activity relationship (QSAR) models were developed for dihydrofolate reductase (DHFR) inhibition by pyrimethamine derivatives using small molecule descriptors derived from MOE and/or QikProp and linear or nonlinear modeling. During this analysis, the best QSAR models were identified when using MOE descriptors and nonlinear models (artificial neural networks) optimized by evolutionary computation. The resulting models can be used to identify key descriptors for DHFR inhibition and are useful for high-throughput screening of novel drug leads.
authors.affiliation: Southwestern College, 900 Otay Lakes Road, Chula Vista, CA 91910 USA. dhecht@swccd.edu
authors.forename: David
authors.initials: D
authors.lastname: Hecht
authors.forename: Mars
authors.initials: M
authors.lastname: Cheung
authors.forename: Gary B
authors.initials: GB
authors.lastname: Fogel
mesh_terms: D000818:Animals; D005493:Folic Acid Antagonists / Q000737:chemistry / Q000494:pharmacology*; D016571:Neural Networks, Computer; D010963:Plasmodium falciparum / Q000201:enzymology*; D011739:Pyrimethamine / Q000737:chemistry / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship; D013762:Tetrahydrofolate Dehydrogenase / Q000187:drug effects* / Q000235:genetics
chemical_list: D005493:Folic Acid Antagonists; D013762:Tetrahydrofolate Dehydrogenase; D011739:Pyrimethamine