title: Development of neural network QSPR models for Hansch substituent constants. 2. Applications in QSAR studies of HIV-1 reverse transcriptase and dihydrofolate reductase inhibitors.
abstract: In this paper, the applications of a Hansch substituent constant predictor(1) to Quantitative Structure-Activity Relationships (QSAR) studies of E. coli dihydrofolate reductase (DHFR) inhibitors 2,4-diamino-5-(substituted-benzyl)pyrimidines as well as HIV-1 reverse transcriptase (RT) inhibitors 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives are demonstrated. Both data sets contain functional groups for which the substituent constants (pi, MR, F and R) could not be found in standard substituent constant tables. The substituent constant predictor allowed us to derive predicted pi, MR, F and R values for all substituents in both data sets, thus enabling the generation of easily interpretable QSAR models of comparable or better predictivity than previous models.
authors.affiliation: Roche Research Center, Hoffmann-La Roche Inc, Nutley, New Jersey 07110, USA.
authors.forename: Ting-Lan
authors.initials: TL
authors.lastname: Chiu
authors.forename: Sung-Sau
authors.initials: SS
authors.lastname: So
mesh_terms: D005493:Folic Acid Antagonists / Q000138:chemical synthesis* / Q000494:pharmacology*; D009415:Nerve Net; D021281:Quantitative Structure-Activity Relationship; D018894:Reverse Transcriptase Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D013762:Tetrahydrofolate Dehydrogenase / Q000187:drug effects*
chemical_list: D005493:Folic Acid Antagonists; D018894:Reverse Transcriptase Inhibitors; D013762:Tetrahydrofolate Dehydrogenase