A Comprehensive Review on Artificial Intelligence for Accelerating Drug Discovery and Protein Design

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in the field of drug discovery and protein design, offering innovative solutions to longstanding challenges in biomedical research. Traditional drug discovery approaches, reliant on trial-and-error, high-throughput screening, and structural biology, are often time-consuming, costly, and associated with high failure rates. AI-driven technologies, particularly machine learning (ML), deep learning (DL), and natural language processing (NLP), enable the rapid analysis of large-scale biological datasets, prediction of drug-target interactions, and design of novel compounds with optimized therapeutic potential. In drug discovery, AI contributes to target identification, virtual compound screening, lead optimization, and clinical trial design by predicting pharmacokinetics, toxicity, and efficacy at early stages. Similarly, in protein design, tools such as AlphaFold and Rosetta have revolutionized structure prediction and facilitated de novo design of functional proteins, enabling the development of novel enzymes, antibodies, and therapeutic proteins. These advances accelerate precision medicine by tailoring drugs to individual genetic and proteomic profiles while reducing development timelines and costs. Despite these breakthroughs, significant challenges remain, including the need for high-quality annotated datasets, computational limitations, biological complexity, and regulatory concerns surrounding AI-generated drug candidates. Addressing these barriers will be critical to harnessing AI’s full potential in biopharmaceutical innovation. Overall, AI-driven drug discovery and protein design represent a paradigm shift, promising faster, cost-effective, and more precise therapeutic development with profound implications for global healthcare.