Molecular Modeling Study for the Design of New TRPV4 Antagonists Using 3D-QSAR, Molecular Docking Molecular Dynamic, ADMET Prediction and Retrosynthesis

TRPV4 antagonists that could be potential drugs for pain management. This study uses advanced computational analysis techniques, including 3D-QSAR modelling, molecular docking and assessment of pharmacokinetic properties (ADMET), to identify novel ligands with potent TRPV4 antagonistic activity of on various arylsulfonamide derivatives. We developed an optimal 3D-QSAR model using partial least squares analysis (PLS) and comparative molecular similarity coefficient analysis (CoMSIA), obtaining excellent correlation and predictive power (R² = 0.953, Q² = 0.747 and SEE = 0.072). Our results highlight the significant roles of electrostatic and hydrophobic fields, as well as hydrogen bond acceptors and donors, in influencing variations in the observed biological activity. 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 were subjected to MD simulation to confirm their dynamic behaviour and stability, and were analysed retrosynthetically to guide their synthesis.