Machine Learning-Based Drug Repositioning of Novel Human Aromatase Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

In the continuous search for potential drug-like molecules for diseases, drug repurposing offers a solution by identifying new roles for the existing drugs. For breast cancer, which is the leading cause of death among women, discovering a new drug with fewer side effects can take more than a year, and drug repurposing provides a faster alternative. In this study, we used machine learning (ML) and structure-based screening to evaluate molecules from the ChEMBL library against Human Aromatase, an enzyme that is involved in the conversion of androgens to estrogen. The virtual screening output identified 148 potential compounds. These compounds were further subjected to detailed structure-based virtual screening, followed by assessment of induced fit docking, quantum-based docking and ADMET properties. The top compound, CHEMBL502014 and the co-crystallized ligand (reference) were evaluated through MD simulations for 100 ns. The results indicated that the CHEMBL502014-Aromatase complex demonstrated better conformational stability throughout the simulation compared to the reference ligand. Based on these analyses, we propose CHEMBL502014 and other top compounds with known activities against various proteins as potential candidates for anti-cancer activity against breast cancer. These compounds should be further evaluated using experimental assays to confirm their effectiveness against breast cancer.