INTEGRATION OF MOLECULAR DOCKING AND MACHINE LEARNING FOR DRUG REPURPOSING IN THE DISCOVERY OF SARS-COV-2 ANTIVIRAL AGENTS - Hidayat, Gabriel
SARS-CoV-2 has emerged as a global pandemic, claiming approximately 6 million lives annually by the year 2023. First identified in December 2019 in Wuhan, China, the rapid spread of this virus has rendered it perilous, prompting researchers to fervently seek antiviral remedies. Urgent steps are required in the discovery of new drugs, leading to the adoption of drug repurposing through in silico methods to expedite the process. This study employs molecular docking and machine learning techniques to assess the binding affinity between drug molecules and the target proteins of SARS-CoV-2. Molecular docking is executed using YASARA software, while machine learning leverages the Deepchem library, specializing in drug repurposing and design. The outcomes obtained through both methods manifest as binding affinity, representing the strength of interaction between a molecule and the active site of its target protein. Six target proteins (1s9p, 5r7y, 5rl6, 5s6x, 6m71, and 7rgq) are selected for this study, with a total of 82 antiviral drugs and 45 anticancer drugs employed. Molecular docking results provide binding affinity values for each ligand-protein interaction, which are subsequently used to construct a random forest model for predictions. Through molecular docking, Remdesivir is identified as the drug with the highest binding affinity for protein target 1s9p, Rimantadine for 5rl6 and 5s6x, and Oseltamivir for 5r7y, 6m71, and 7egq. The machine learning model, utilizing the random forest algorithm, demonstrates effective predictive capabilities with a mean absolute error (MAE) of 0.484365 and an average ranking difference with molecular docking of 4.02. This suggests that the machine learning model is proficient in predicting potential drug candidates, providing valuable insights for drug repurposing against SARS-CoV-2