The Process of AI-Aided Drug Design
DOI:
https://doi.org/10.47611/jsrhs.v12i4.5630Keywords:
AI, Artificial Intelligence, Drug Design, Machine Learning, Biomedicine, Biotechnology, Bioinformatics, Biology, Science, Computer Science, Chemistry, Biochemistry, Biomedical EngineeringAbstract
Artificial Intelligence (AI) is a growing field in today’s world and plays a part in many industries today. Its role in drug design and the biological sciences has begun to expand in recent years. DeepChem is an open source tool that explores and employs the methods behind drug design. The tool’s process and end result will be indicative of how well AI can perform the job of drug discovery and how much it can expedite the process, as well as reveal the future of tools like DeepChem. DeepChem handles everything from data processing to fitting AI models to performing predictions on proposed molecules. By applying DeepChem and reviewing it, we will be revealing AI’s power and limitations in biomedical chemistry and technology. In addition, other AI tools, such as Chemistry 42 and inClinico by Insilico that achieve other parts of the drug design process. Completing a comprehensive review of these methods will provide an overview of what can be improved and the scope of AI as a big money-maker and solution in the biomedical field. The synthesis of drugs is a complicated process that can be simplified by AI tools. This paper explores how AI tools operate and their limitations in the medicinal world.
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Copyright (c) 2023 Aditya Shirolkar; Jothsna Kethar, Rajagopal Appavu
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