QSAR and Docking Studies on Some Potential Anti-Cancer Agents to Predict their Effect on M14 Melanoma Cell Line

Abstract

The global incidence of melanoma cancer is increasing rapidly and its metastatic form causes high mortality rates. The research report illustrates the development of a quantitative structure–activity relationship (QSAR) to predict the activities (pGI₅₀) of some anti-cancer agents on melanoma cancer cell line. Subsequently, most potent compounds that showed better pGI₅₀ activities were selected and screened via Lipinski’s rule of five for drug likeness, ADMET risk parameters and lastly docking simulation studies was performed to elucidate their binding mode. Kennard-Stone algorithm was adopted for the data division, the genetic function algorithm was employed for variable selection and multiple linear regressions method was utilized for model development. The derived QSAR model showed, respectively acceptable [($R^{2}$ (0.805), $R_{adjusted}^{2}$ (0.773), Q²_cv (0.754) and $R_{pred}^{2}$ (0.703)]. The obtained cR²_P for Y-randomization is 0.649, and applicability-domain (A-D) was assessed via leveraged method. Among the screened compounds via Lipinski’s rule of five for oral bioavailability, ADMET risk filter for drug-like features and Docking simulation studies, compound 18 and 25 were identified as the best ligands having the better interactions energies (− 150.679 kcal mol⁻¹ and − 176.246 kcal mol⁻¹) and showed better interactions with the receptor than vemurafenib (− 147.245 kcal mol⁻¹). Thus, the results of this research would be helpful in identification of lead molecule and optimization of novel drug.