Inhibition activity prediction for a dataset of candidates’ drug by combining fuzzy logic withMLR/ANN QSARmodels

Abstract

A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole‐based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effective capability of handling and processing information about the real world, in this case, the fuzzy set theory was introduced into the QSAR. An integration of multiple linear regression and artificial neural network with adaptive neuro‐fuzzy inference systems (ANFIS) was developed to predict the inhibition activity. The algorithm of ANFIS was applied to identify the suitable variables and then to find the optimal descriptors. The gradient descent with momentum backpropagation ANN was used to establish the nonlinear multivariate relationships between the chemical structural parameters and biological response. A comparison between the result of the proposed linear and nonlinear regression showed the superiority of QSAR modeling by ANFIS‐ANN method over the MLR. The results demonstrated that the ANFIS could be applied successfully as a feature selection. The appearance of Diam, Homo, and LogP descriptors in the model showed the importance of the steric, electronic, and thermodynamic interactions between a drug and its target site in the distribution of a compound within a biosystem and its interaction with competing for binding sites.

QSPR/QSAR: State-of-Art, Weirdness, the Future

“…This fact logically echoes the theory of fuzzy sets [108]. This is not surprising, as fuzzy set theory has success in solving some problems of QSPR/QSAR analysis [109][110][111].…”

QSPR/QSAR: State-of-art, Weirdness, the Future

“…This fact logically echoes the theory of fuzzy sets [108]. This is not surprisingly; fuzzy set theory has success for solving of some problems of QSPR/QSAR analysis [109][110][111].…”

Opportunities and challenges in application of artificial intelligence in pharmacology

“…ANN the most popular paradigm for nonlinear modelling in QSAR aims to imitate the human nervous system workflow and contains several neuron layers. ANN is integrated with adaptive neuro-fuzzy inference systems and multiple linear regression (MLR) to the dataset consisting of 90 pyridinylimidazole‐based compounds (inhibitors of p38Rmitogen‐activated protein kinases); the performance of ANN was a better predictor model (ANN vs MLR, R 2 training: 0.8520, 0.4049, respectively) to establish physicochemical properties and output descriptors relationship [ 83 ]. A study optimized ANN architectures and interpreted six differnt methods (partial derivative-PaD, pairwise partial derivative, weights, perturbation, profile methods, and sum of ranking differences analysis) to figure out the relationship between quantum mechanical molecular descriptors and output (Trolox‐equivalent antioxidant capacity of 33 flavonoids).…”