AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways
Abstract
Preeclampsia is a hypertensive disorder of pregnancy marked by angiogenic imbalance and endothelial dysfunction, driven largely by over expression of soluble fms-like tyrosine kinase-1 (sFlt-1) and up-regulation of endothelin-1 (ET-1). Targeting both VEGFR1 and ET-1 receptor could offer a dual-action therapeutic approach. In this study, macitentan, a dual ETA/ ETB antagonist, was used as a scaffold for AI-assisted derivative generation aimed at dual inhibition of these receptors. A comprehensive computational pipeline was applied including molecular docking, pharmacophore modeling, molecular dynamics (MD) simulation, DFT, MM/GBSA, and ADMET analysis. Derivative 24 showed high binding affinity toward VEGFR1 (-7.7 kcal/mol), while Derivative 15 exhibited superior interaction with ET-1 receptor (-9.2 kcal/mol), both outperforming macitentan. MD simulations over 500 ns confirmed complex stability with RMSD stabilization around 0.2-0.3 nm and consistent hydrogen bonds. MM/GBSA binding energies further supported strong receptor interactions (-17.15 kcal/mol for Derivative 24-VEGFR1; -28.72 kcal/mol for Derivative 15-ET-1 receptor). DFT results showed reduced HOMO-LUMO gaps (3.55 eV for Derivative 24; 3.30 eV for Derivative 15), while MEP analysis indicated favorable electrostatic potential for target interaction. Pharmacophore and ADMET profiling revealed improved drug-likeness, high GI absorption, and reduced predicted toxicity. This study presents Derivatives 15 and 24 as…
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