In our study, we complemented this modern approach by integrating it with quantitative structureactivity relationship (QSAR) modeling and quantitative high-throughput screening (qHTS). This combination allowed for a more systematic and data-informed identification of potential drug compounds . Our methodology was further enriched by incorporating extensive chemical databases . These databases, replete with molecular building blocks and reaction templates offer a rich resource for data-driven drug design, facilitating the exploration of a wide range of chemical structures and properties . QSAR modeling has been successfully used in the past for virtual screening, where it helps predict the biological activity of compounds based on their chemical structures . This prediction capability of QSAR models aids in narrowing down the vast pool of potential compounds to those most likely to exhibit desired biological activities .