Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
This paper systematically reviews recent advances in artificial intelligence
(AI), with a particular focus on machine learning (ML), across the entire drug
discovery pipeline. Due to the inherent complexity, escalating costs, prolonged
timelines, and high failure rates of traditional drug discovery methods, there
is a critical need to comprehensively understand how AI/ML can be effectively
integrated throughout the full process. Currently available literature reviews
often narrowly focus on specific phases or methodologies, neglecting the
dependence between key stages such as target identification, hit screening, and
lead optimization. To bridge this gap, our review provides a detailed and
holistic analysis of AI/ML applications across these core phases, highlighting
significant methodological advances and their impacts at each stage. We further
illustrate the practical impact of these techniques through an in-depth case
study focused on hyperuricemia, gout arthritis, and hyperuricemic nephropathy,
highlighting real-world successes in molecular target identification and
therapeutic candidate discovery. Additionally, we discuss significant
challenges facing AI/ML in drug discovery and outline promising future research
directions. Ultimately, this review serves as an essential orientation for
researchers aiming to leverage AI/ML to overcome existing bottlenecks and
accelerate drug discovery.
