Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.

Abstract

The integration of artificial intelligence (AI) into oncology drug discovery is redefining the traditional pipeline by accelerating discovery, optimizing drug efficacy, and minimizing toxicity. AI has enabled groundbreaking advancements in molecular modeling, simulation techniques, and the identification of novel compounds, including anti-tumor and antibodies, while elucidating mechanisms of drug toxicity. Additionally, AI has emerged as a critical tool in precision medicine, driving the formulation and release of targeted therapies and improving the development of treatments for oncology and central nervous system diseases. Furthermore, AI-assisted clinical trial designs have further optimized the recruitment and stratification of patients, reducing the time and cost of trials. Despite these advancements, challenges such as data integration, transparency, and ethical considerations persist. By synthesizing current innovations, this manuscript provides a comprehensive analysis of AI-driven approaches in drug discovery and their potential to advance oncology therapeutics and precision medicine. It examines the transformative role of AI across the drug development continuum, with a focus on its applications in computer-aided drug design (CADD), generative artificial intelligence (GAI), and high-throughput screening (HTS).