Prediction of Drug Likeness and Synthetic Accessibility Using AI: A Case Study on Potential Therapeutic Compounds from Plants for SARS-CoV-2 Treatment
摘要
As scientists learn more about SARS-CoV-2, they are exploring plant-based compounds as possible treatments. These compounds may block the proteins the virus needs to multiply and enter cells. Traditional remedies like green and black tea, Terminalia chebula (haritaki), and various spices have shown potential in stopping the virus and supporting the immune system. Computer modeling has identified plant chemicals such as taraxerol, friedelin, stigmasterol (from Clerodendrum species), and EBDGp (from Phyllanthus emblica and Aegle marmelos) as strong binders to important SARS-CoV-2 proteins. In this study, we use artificial intelligence to help find and improve these plant-based candidates. AI tools can help design these compounds, check if they could work as drugs, and see how easy they are to make. This method helps move promising compounds from computer models into real-world drug development.