Discovery of a New Mcl‐1 Protein Inhibitor through the QSAR Approach and Molecular Docking Study

Abstract

In this study, a quantitative structure‐activity relationship (QSAR) model of anticancer activity against myeloid cell leukemia 1 (Mcl‐1) for a series of 41 tricyclic indole diazepinone derivatives is established. Three different modeling methods, multiple linear regression (MLR), partial least square (PLS), and artificial neural network (ANN) are investigated to perform a QSAR model with significant predictiveness. A clustering method is also used for dividing all compounds into training and external test (ET) sets. Component principal analysis is used to eliminate the redundancy between descriptors. The accuracy and predictability of the proposed models are proven by comparing their key statistical terms. The good results obtained with the internal and external validations (EV) show that the proposed models can predict high‐performance activities and that the selected descriptors are pertinent. This model is also validated using internal validation (IV), mainly using cross‐validation (leave‐many‐out (LMOCV)). The applicability domain (AD) is identified. Based on the SAR map analysis, a novel Mcl‐1 inhibitor with a good predicted activity using the best model is proposed, the interaction of the designed compound with the binding site of Mcl‐1 protein is evaluated and its docking score is found high.