Computational Drug Repurposing and Natural Compound Discovery in Precision Medicine: Molecular Targeting Across Cancer, Neuroinflammation, and Antimicrobial Resistance
DOI:
https://doi.org/10.31149/ijimm.v3i12.2679Keywords:
Computational Drug, Artificial Intelligence, Precision Medicine, Machine LearningAbstract
Computational drug repurposing and the search for natural compounds are changing the landscape of precision medicine. Instead of relying solely on the lengthy, expensive process of developing new drugs from scratch, these approaches leverage what we already know about existing drugs and the natural world. This means treatments can be discovered faster, more affordably, and with fewer risks. The focus is on how natural compounds that target multiple pathways, and on existing drugs used in new ways, can help address diseases with many causes. There is also growing interest in using new delivery methods, like nanotechnology, to make these compounds even more effective in real-world treatment. Even with all these advances, there are still some big challenges to overcome. Issues such as data quality, understanding how AI models make decisions, and addressing legal and ethical questions are important hurdles. To move forward, experts from different fields need to work together, create clearer rules, and improve how data is shared and understood. Looking ahead, combining data from many sources, improving AI models that predict how drugs work in the body, and building better ways for teams to collaborate will help push the field further. Overall, using computers to find new uses for drugs and discover helpful natural compounds offers a promising, flexible way to accelerate the development of new treatments and make medicine more personal for everyone.
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