In silico design of endothelin receptor antagonists using Monte Carlo-based QSAR modeling, molecular docking, and ADME profiling

Abstract

Endothelin-1 (ET-1), a potent vasoconstrictor peptide, plays a critical role in cardiovascular pathologies and remains a key target for therapeutic intervention. Despite its clinical significance, the development of selective and potent ET-1 antagonists continues to present major challenges. Computational methods, particularly Quantitative Structure–Activity Relationship (QSAR) modeling, offer a rational and efficient framework for designing such compounds. In this study, conformation-independent QSAR models were developed using molecular descriptors derived from SMILES notation and local molecular graph invariants. The Monte Carlo method was employed for descriptor selection and weight optimization, resulting in statistically robust models. Critical molecular fragments associated with antagonist activity were identified and applied in the computer-aided design (CAD) of new ET-1 inhibitors. The optimal QSAR model exhibited strong predictive performance, with high correlation coefficients for both the training set (r² = 0.9362, q² = 0.9314) and the test set (r² = 0.9006, q² = 0.8655). To further validate the structural plausibility of the designed molecules, molecular docking simulations were conducted against the ETA receptor. The docking results were in agreement with QSAR-predicted activity, revealing favorable binding poses, strong interaction energies, and consistent structure–activity trends across all six designed compounds. This methodological convergence strengthens the credibility of the in silico predictions. Additionally, computational analysis of physicochemical and pharmacokinetic parameters indicated favorable ADME profiles, high drug-likeness, and efficient gastrointestinal absorption, suggesting suitability for medicinal chemistry development. This study introduces a reliable and mechanistically interpretable computational pipeline for the discovery of novel ET-1 antagonists. The proposed compounds demonstrate promising pharmacological characteristics and represent viable candidates for future experimental validation. These findings underscore the value of integrated in silico strategies in accelerating cardiovascular drug discovery.