Volume 1 Issue 3 | 2024 | View PDF
Paper Id: IJMSM-V1I3P102
doi: 10.71141/30485037/V1I3P102

Transforming Pharmaceutical R&D with Machine Learning: Advances in AI-Driven Drug Design

Santhosh

Citation:
Santhosh, "Transforming Pharmaceutical R&D with Machine Learning: Advances in AI-Driven Drug Design" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 3, pp. 14-28, 2024.

Abstract:
Machine learning technologies have changed the curve in pharmaceutical research and development (R&D). Taking advantage of these advancements, more efficient drug discovery, prediction of molecular interactions, and faster identification of possible therapeutic candidates were possible. In this paper, recent inventions in AI based drug design are explored, and the use of deep learning algorithms, generative modeling, and structure based drug discovery are exhibited. Finally, case studies highlight the capability of ML approaches to alleviate the traditional R&D limitations: high attrition rate, long development time, and increasing costs. We also discuss the issues of bringing AI into the pharmaceutical pipeline, data quality, interpretability, and regulatory issues, as well as the potential for AI to transform personalized medicine in the future.

Keywords: Machine Learning, Drug Discovery, AI-Driven Drug Design, Pharmaceutical R&D, Generative Models, Predictive Modeling, Personalized Medicine.

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