AI-Driven Drug Discovery: Speeding Up the Race for Novel Therapies in Precision Medicine
DOI:
https://doi.org/10.63530/IJCSITR_2025_06_03_006Keywords:
Clinical trials, precision medicine, machine learning, personalized therapy, drug design, target identification, AI in drug discoveryAbstract
The pharmaceutical sector is being changed by the inclusion of artificial intelligence (AI) into drug discovery, which has the possibility to greatly speed the creation of new treatments, especially in the framework of precision medicine. Various phases of drug discovery including target identification, compound screening, clinical trial optimization, and individualized therapy creation are being leveraged by artificial intelligence technologies such as machine learning, deep learning, and natural language processing. AI can find potential drug targets, create molecules with wanted qualities, and simplify the clinical trial process by processing large volumes of biological and clinical data. With particular emphasis on its contribution to precision medicine where treatments are customized to the individual traits of patients, including their genetic makeup, disease subtype, and environmental factors this paper investigates the uses of artificial intelligence in drug discovery. Although AI-driven drug discovery offers great potential, issues including data quality, algorithm transparency, and ethical questions must be resolved. By lowering costs and time, enhancing therapeutic outcomes, and developing more efficient treatments, this paper also explores how artificial intelligence might change the future of drug discovery. AI-driven innovations will increasingly determine the future of precision medicine by delivering very targeted and individualized therapies.
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Copyright (c) 2025 Narendra Chennupati (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
