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Artificial Intelligence (AI) is quickly becoming a powerful ally in the search for new medicines. From
identifying potential drug candidates to predicting how they’ll behave in the body, AI is helping researchers
speed up the discovery process and reduce costs. This review looks at how AI is being used in drug discovery,
what’s working well, and what challenges still need to be addressed. While AI brings exciting possibilities, its
success depends heavily on access to quality data, the transparency of its decision-making, and how well it works
alongside traditional lab-based methods. We also explore important concerns around ethics, model bias, and the
current limits of AI technologies. Along the way, we highlight strategies like explainable AI, data augmentation,
and combining AI tools with human expertise to get the best results. Overall, this paper aims to give a clear
picture of both the promise and the reality of using AI in drug discovery—and how we might move forward,
smarter and faster.
Keywords:
artificial intelligence, drug discovery, machine learning, explainable AI, pharmaceutical research, challenges and opportunities
Cite Article:
"Molecules to Medicines: AI’s Transformative Role in Drug Discovery", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a805-a811, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505095.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator