Revisiting BACE-1: How Machine Learning and Molecular Dynamics Unveiled Potential Anti-Alzheimer’s Activity of a Cysteinyl Sulfoxide Derivative

Beta Secretase (BACE1) is a well-validated target for Alzheimer’s therapies, but there has been attrition in drug development. Herein, we leveraged machine learning (ML), virtual screening and molecular dynamics (MD) to identify novel compounds with potential activity against BACE1. We developed ML algorithms to distinguish active and inactive compounds from public databases. Molecular docking and dynamics were used to explore the inhibition mechanism, thermodynamic stability, and the flap dynamics of the BACE1-ligand complexes. Random Forest Classifier (RF) showed excellent metrics (accuracy: 0.9807; F1 score: 0.9804; specificity 0.9977), compared to other models. Molecular docking with predicted actives revealed compounds BA1, BA2, and BA3 with strong affinity for BACE1. Compound BA2, a cysteinyl sulfoxide derivative, showed good stability (RMSD) during simulations (1.307 ± 0.109 Å) compared to Verubecestat (1.602 ± 0.159 Å). MMGBSA-based binding free energy (ΔGbind; kcal/mol) showed that BA2 (−33.820 ± 4.254) had comparatively lower energy than Verubecestat (−21.090 ± 6.183). BA2 maintained electrostatic interactions with the catalytic dyad (Asp36 and Asp232) and Thr76 of the flap. BA2 also maintained the flaps in a semi-open conformation (d0: 11.807 ± 0.401 Å) throughout the simulation. Our study clearly demonstrates the utility of ML in prioritization of compounds before molecular docking and MD in early phases of drug discovery.