Adaptive neuro-fuzzy inference system-applied QSAR with bond dissociation energy for antioxidant activities of phenolic compounds - Jhin, Changho; Nho, Chu Won; Hwang, Keum Taek (PHARMACEUTICAL SOC KOREA, 2017)
The aim of this study was to develop quantitative structure-activity relationship (QSAR) models for predicting antioxidant activities of phenolic compounds. The bond dissociation energy of O-H bond (BDE) was calculated by semi-empirical quantum chemical methods. As a new parameter for QSAR models, sum of reciprocals of BDE of enol and phenol groups (X (BDE) ) was calculated. Significant correlations were observed between X (BDE) and antioxidant activities, and X (BDE) was introduced as a parameter for developing QSAR models. Linear regression-applied QSAR models and adaptive neuro-fuzzy inference system (ANFIS)-applied QSAR models were developed. QSAR models by both of linear regression and ANFIS achieved high prediction accuracies. Among the developed models, ANFIS-applied models achieved better prediction accuracies than linear regression-applied models. From these results, the proposed parameter of X (BDE) was confirmed as an appropriate variable for predicting and analysing antioxidant activities of phenolic compounds. Also, the ANFIS could be applied on QSAR models to improve prediction accuracy.
Keywords: SEMIEMPIRICAL METHODS; THEORETICAL ELUCIDATION; NDDO APPROXIMATIONS; ENTHALPIES; DPPH; DFT; OPTIMIZATION; PARAMETERS; MECHANISM; CAPACITY