TY - JOUR
T1 - Deep Learning for Oncology Drug Discovery: A Systematic Review of Target Identification, Compound Screening, and Clinical Trial Optimization
AU - Sophie Laurent
AU - Pierre Dubois
AU - Marc Lefevre
AU - Claire Moreau
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2026
VL - 5
IS - 1
SP - 122
N2 - Oncology drug development is an expensive and high-failure process, with costs exceeding two billion dollars per approved drug and success rates below 10%. Deep learning has recently been explored as a strategy to improve efficiency across the drug discovery pipeline. This systematic review evaluates its application in target identification, compound screening and de novo drug design, and clinical trial optimization. Following PRISMA 2020 guidelines, multiple databases were searched and studies were screened using predefined inclusion criteria, with risk of bias assessed via established tools. The literature shows that graph neural networks and transformer-based models are the most widely used architectures, particularly in early-stage discovery tasks. Although many studies report strong in silico performance, often with AUC values above 0.80, only a small proportion demonstrate experimental or clinical validation. Overall, deep learning significantly advances computational drug discovery in oncology, but translation into clinically validated therapies remains limited, especially in trial optimization, highlighting the need for stronger prospective and experimental validation frameworks.
UR - https://cirpublications.com/t715039840
ER -