An integrated computational approach combining QSAR modeling, molecular docking, and ADME profiling for the discovery of selective CYP11B1 inhibitors

Abstract

CYP11B1 inhibitors play a critical role in controlling cortisol biosynthesis and represent promising therapeutic candidates for disorders such as Cushing’s syndrome and hypertension. In this study, a series of novel analogues were designed and evaluated using an integrated approach combining quantitative structure–activity relationship (QSAR) modeling, molecular docking, and ADME predictions. Multiple linear regression (MLR), partial least squares (PLS), and principal component regression (PCR) models were constructed to establish robust predictive relationships between molecular descriptors and inhibitory activity against CYP11B1. The models were rigorously validated through external test-set prediction, Y-randomization, and applicability-domain (AD) analysis, all satisfying OECD criteria (R² = 0.725–0.772, Q² = 0.701–0.752, RMSE = 0.242–0.310). Docking simulations revealed that compound D3 exhibited the most favorable binding affinity (−7.45 kcal/mol) and formed stable π–H and π–cation interactions with key residues Arg404 and Leu113, suggesting selective inhibition of CYP11B1. ADME and drug-likeness evaluation indicated predicted favorable pharmacokinetic properties, including high gastrointestinal absorption, absence of blood–brain barrier penetration, and good solubility, with D3 also demonstrating the lowest synthetic-accessibility score (SA = 3.09). Overall, this integrated computational approach successfully identified D3 as a potent and synthetically feasible CYP11B1 inhibitor candidate. These findings provide a validated framework for the rational design and optimization of new inhibitors with improved pharmacological and metabolic profiles.