The Future of AI in Drug Discovery: A Review of Deep Learning and Computational Approaches

Abstract

AI is revolutionizing drug discovery by accelerating target identification, molecular design, and clinical trials. This paper reviews machine learning and deep learning techniques used in computational drug screening and biomarker identification. We explore AI applications in protein structure prediction, generative models for molecule synthesis, and virtual screening. Challenges such as data availability, model generalization, and ethical considerations are discussed. Future research directions highlight the integration of AI with quantum computing and personalized medicine.