Enhanced QSAR Modeling for Drug Discovery: Leveraging Advanced Computational Tools and Techniques

In this research, we present a comprehensive Quan-titative Structure-Activity Relationship (QSAR) methodology uti-lizing advanced computational tools and techniques for drug discovery targeting the SARS 3-C like proteinase. By integrating data retrieval from the ChEMBL database, calculation of Lipin-ski parameters, molecular fingerprint generation using PaDEL, and Gaussian Process Regressor modeling, we aimed to enhance the predictive accuracy and reliability of QSAR models. Our results demonstrate the effectiveness of the proposed method-ology in accurately predicting compound bioactivity against the target protein. The Gaussian Process Regressor, with its probabilistic modeling capabilities and flexibility in capturing complex nonlinear relationships, proved to be a suitable choice for modeling intricate interactions between chemical structures and bioactivities. This approach offers a promising foundation for future research and advancements in computational drug design, potentially accelerating the development of novel therapeutic agents for various diseases.