Computational Investigation with Toxicophore Study of 1,2,3-Triazole Derivatives as an Effective Inhibitor Against Prostate Cancer

Abstract

Prostate cancer is a well-known disease that has gained significant attention in recent years. To improve and suggest new compounds with anticancer activity, it has become essential to identify new proposed agents through innovative and reliable methods such as computational small molecule discovery methods. In this regard, 3D-QSAR and Molecular Docking studies have been conducted on disubstituted 1,2,3-triazole derivatives as antiproliferative analogs, using static methods to find the right model. The study established 3D-QSAR model based on Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA). The best model was obtained with CoMFA model (Q² = 0.696, R² = 0.992, R = 0.985) and CoMSIA model (Q² = 0.582, R² = 0.992, R = 0.984) statistical coefficients. To determine the predictive power of the model, we need to calculate the parameters of k, Roy, Golbraikh, and Tropsha for the test set and the y, SEE, and t-F randomization tests for the training set. Docking’s results suggest that amino acids (PDB; 3 ERT), Asp351, Leu384, Arg394, Phe404, Leu346, Leu525, and Thr347, have a significant interest in anticancer activity. The CoMFA model’s steric and electrostatic field contours were studied to determine the results further. The study suggests four new antiproliferative agents that have demonstrated reliability through ADMET and toxicophore methods.