Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy

• URL: http://arxiv.org/abs/2507.03407v1
• Date: Fri, 04 Jul 2025 09:14:56 GMT
• Title: Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy
• Authors: Junwei Su, Cheng Xin, Ao Shang, Shan Wu, Zhenzhen Xie, Ruogu Xiong, Xiaoyu Xu, Cheng Zhang, Guang Chen, Yau-Tuen Chan, Guoyi Tang, Ning Wang, Yong Xu, Yibin Feng,
• Abstract summary: This paper systematically reviews recent advances in artificial intelligence (AI) and machine learning (ML) across the entire drug discovery pipeline.<n>Due to the inherent complexity, escalating costs, prolonged timelines, and high failure rates of traditional drug discovery methods, there is a critical need to understand how AI/ML can be effectively integrated throughout the full process.
• Score: 18.76414044151594
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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