Computational Integration of TRPV4 Antagonists: 3D QSAR, Molecular Docking, Molecular Dynamics Simulations, ADME/Tox, and Retrosynthesis studies

Abstract TRPV4 antagonists that could be potential drugs for treatment and management of neuropathic pain. This study was conducted to systematically design several amino derivatives of 2,4′‐dimethyl‐[4,5′‐bithiazol]‐2‐yl as antagonists. In silico computational methods, such as 3D quantitative structure‐activity relationship modeling (3D‐QSAR), molecular docking, and pharmacokinetic property assessment (ADMET), were used to discover new compounds with strong antagonistic activity. The best‐performing 3D‐QSAR model was developed using a partial least squares approach and comparative molecular similarity analysis (CoMSIA). The developed model demonstrated an excellent ability to correlate and predict properties ( R 2 = 0.932, Q 2 = 0.620, and SEE = 0.109). It was observed that variations in biological activity were significantly influenced by interactions with steric, electrostatic, and hydrophobic fields. Molecular docking was used to validate the 3D‐QSAR methods and to explain the binding site and interactions between the most active ligands and the receptor. Based on these results, a new series of compounds was predicted. The best‐anchored molecules underwent MD simulation to confirm their dynamic behavior and stability, and they also underwent retrosynthetic analysis to assist in their synthesis.