Integrated Computational Design and Discovery of Novel Indole-Linked Guanidine Derivatives as BACE-1 Inhibitors for Alzheimer's Disease
Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder predominantly affecting 47 million people worldwide. Dysregulation of BACE-1, the initiating enzyme in the amyloidogenic cascade, plays a pivotal role in AD pathogenesis. Conventional anticholinesterase inhibitors can only provide symptomatic relief without stopping disease progression. In this study, we employed an integrated computational approach to rationally design novel indole-based BACE-1 inhibitors through pharmacophore modeling, 3D-QSAR analysis, ligand-protein docking, pharmacophore-guided virtual screening, MD simulation, and ADME studies. DDHRR_1 was identified as the best pharmacophore model with a survival score of 5.7767. Statistically significant 3D-QSAR models were obtained, wherein the atom-based model demonstrated high predictive reliability (R 2 =0.9386, Q 2 =0.7432), while the field-based model exhibited acceptable predictive ability (R 2 =0.8492, Q 2 =0.6644). Among the dataset compounds, compound B38 (3i) displayed the optimal binding affinity (-6.567 kcal/mol; PDB ID: 4DJU), with key interactions at GLY95, TYR132, and PHE169. R-group enumeration generated 2408 derivatives, among which R1 and R2 exhibited strong binding affinity (-10.339 and -9.611 kcal/mol). A comparative analysis of the referenced ligand, the two best-performing ZINC-derived compounds (ZINC43707325 and ZINC03628223), and the two top-ranked R-group compounds revealed that R2 was the major lead compound. However, as these findings are based solely on in-silico analyses, further synthesis, biological evaluation, and BBB permeability studies are necessary to validate the therapeutic potential of R2 against Alzheimer's disease.