AI-Driven Drug Discovery: Accelerating Pharmaceutical Research with Deep Learning

Abstract

The integration of artificial intelligence (AI) in drug discovery is revolutionizing pharmaceutical research by reducing time and costs associated with developing new medications. This paper explores deep learning techniques for molecular modeling, protein structure prediction, and drug-target interaction analysis. We propose a generative AI framework using Graph Neural Networks (GNNs) and Transformer models to accelerate the identification of promising drug candidates. Experimental results demonstrate improved accuracy in predicting drug efficacy and reducing false positives. The study also discusses regulatory challenges, data limitations, and ethical concerns in AI-driven drug discovery.