Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design - Albani FG; Alghamdi SS; Almutairi MM; Alqahtani T (Drug Design, Development and Therapy)
Fatimah G Albani,1,2 Sahar S Alghamdi,2– 4 Mohammed M Almutairi,5 Tariq Alqahtani2– 4 1Department of Biology, Faculty of Science, Princess Nourah bint Abdulrahman University, Al-Riyadh, Saudi Arabia; 2Medical Research Core Facility and Platforms, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia; 3College of Pharmacy (COP), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Kingdom of Saudi Arabia; 4King Abdulaziz Medical City, Ministry of the National Guard - Health Affairs, Riyadh, 11426, Saudi Arabia; 5Pharmacology and Toxicology Department, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi ArabiaCorrespondence: Tariq Alqahtani, Email qahtanita@ksau-hs.edu.saAbstract: 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).Keywords: artificial intelligence, oncology drug discovery, target identification, machine learning in oncology