Prediction of Novel TRPV1 Antagonist: A Combination of 3D-QSAR, Molecular Docking, MD Simulations and ADMET Prediction

TRPV1 are ion channels capable of sensing different stimuli, integrating and translating them into signal language. TRPV1 antagonists have attracted much attention for the treatment of various diseases, due to their properties for the management of pain physiology and neurogenic inflammation such as anti-inflammatory, antineoplastic, and anti-nociceptive. Here, we performed a three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) simulation on a novel series of indole triazole derivatives as antagonists of TRPV1. The aim was to design novel potent TRPV1 antagonists with strong inhibitory activity. The significant 3D-QSAR models showed a good correlation between experimental and predicted activity. Comparative molecular similarity index analysis (CoMSIA) was used to construct the best 3D-QSAR model using the PLS method with correlative and predictive ability (R-2 = 0.985. Q(2) = 0.788. SEE = 0.105). Electrostatic, steric, and hydrophobic fields played an important role in the variation of biological activity of the three main components. Molecular Docking analysis was used to validate the 3D-QSAR methods and explain the binding site and interactions between the most active ligands and the receptor. Based on these results, a novel series of compounds were predicted. The pharmacokinetic properties of predicted compounds were analysed by drug-likeness and ADMET prediction. The best-docked compounds were studied by MD simulation to affirm the final candidate molecules' conformational feature to confirm their dynamic behavior and stability.