Moon, T Chi, MH Kim, DH Yoon, CN Choi, YS 2024-01-21T14:02:58Z 2024-01-21T14:02:58Z 2021-09-05 2000-06 0931-8771 https://pubs.kist.re.kr/handle/201004/141347 The quantitative structure-activity relationships (QSAR) studies on flavonoid derivatives as cytochrome P450 1A2 inhibitors were performed using multiple linear regression analysis (MLR) and neural networks (NN). The results of MLR and NN show that Hammett constant, the highest occupied molecular orbital energy (HOMO), the nonoverlap steric volume? the partial charge of C-3 carbon atom, and the HOMO pi coefficients of C-3, C-3&apos; and C-4&apos; carbon atoms of flavonoids play an important role in inhibitory activity. The correlations between the descriptors and the activities were improved by neural networks although the descriptors of optimum MLR model were used in the networks, which implies that the descriptors used in MLR model include nonlinear relationships. Moreover, neural networks using descriptors selected by the pruning method gave higher r(2) value than neural networks using MLR descriptors. English WILEY-V C H VERLAG GMBH ARTIFICIAL NEURAL NETWORKS PREDICTION Quantitative structure-activity relationships (QSAR) study of flavonoid derivatives for inhibition of cytochrome P450 1A2 Article 10.1002/1521-3838(200006)19:3<257::AID-QSAR257>3.0.CO;2-2 1 QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS, v.19, no.3, pp.257 - 263 QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS 19 3 257 263 scie scopus 000088114900004 2-s2.0-0033934402 Chemistry, Medicinal Pharmacology & Pharmacy Pharmacology & Pharmacy Article ARTIFICIAL NEURAL NETWORKS PREDICTION QSAR multiple linear regression analysis (MLR) neural networks (NN) flavonoids cytochrome P350 1A2