AI-Driven Drug Discovery and Precision Medicine: Revolutionizing Healthcare with Machine Learning

Authors- N.V.S. Sowjanya, Reddy Hema, Dara Twinkle Roy, Gutti Devi Veera Prasanna, Chikkala William Naveen, Bejawada Sai Pavan Rama Krishna

Abstract-Machine Learning (ML) has emerged as a transformative force in drug discovery and personalized medicine, significantly improving efficiency, accuracy, and cost-effectiveness. This study explores the latest advancements in ML applications within these domains, focusing on breakthroughs and challenges. It examines key ML algorithms instrumental in identifying drug candidates, predicting interactions, and understanding disease pathways. Additionally, we propose an innovative framework that integrates advanced ML techniques with large-scale biomedical datasets to enhance drug efficacy predictions and patient-specific treatment responses. A comprehensive literature review outlines major milestones and benchmarks, providing a foundation for our research. Our methodology employs cutting-edge ML models, including deep learning and reinforcement learning, to analyse complex biological data. The effectiveness of our approach is validated through rigorous experimentation on real-world datasets, demonstrating its potential to optimize drug development and enable personalized treatment strategies. The results section provides an in-depth analysis of model performance, supported by statistical tables and graphical representations. Ultimately, this paper highlights the revolutionary impact of ML in reshaping drug discovery and precision medicine while identifying future research opportunities to address existing challenges.

DOI: /10.61463/ijset.vol.13.issue2.227