Autodock is one of the leading and widely used computational tools for molecular docking, majorly played a role in structure-based drug discovery to predict the molecular interactions of small molecules with biological targets. Methodology of Autodock starts with generation of grid-based energy calculation system, which precomputes molecular interaction energies between biological targets and small molecules especially in the protein’s binding sites. AutoDock provides a semi-empirical free energy force field which accounting a van der Waals, hydrogen bonding, electrostatic, desolvation and conformational entropy effects calibrated against experimental data for high predictive accuracy. The screening of optimal ligand poses is predicted with advanced algorithms, like the Lamarckian Genetic Algorithm, which combines both global and local refinement to identify energetically favourable binding modes. In the current study, we exemplify the BACE1 (Beta-site amyloid precursor protein cleaving enzyme 1) a critical drug target in Alzheimer's disease reaching using Autodock, with known small molecule inhibitors was redocked successfully to reproduce the experimental binding mode with sub-2Å RMSD accuracy and offering thorough energetic insights. Such applications demonstrate AutoDOCK’s ability to reliably identify the rank of potential inhibitors, discover novel chemotypes and support virtual screening campaigns with high enrichment rates. The incorporation of GPU acceleration, machine learning applications with enhanced scoring and flexibility in the receptor selection protocols extends the utilization of AutoDock more effectively and making it cornerstone technology for high-accuracy molecular screening in modern drug discovery pipelines.