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

2025-07-04

AI Summary

Traditional drug discovery faces challenges including prolonged development timelines, high costs, and elevated failure rates. Existing AI/ML reviews predominantly focus on isolated stages—such as target identification, hit screening, or lead optimization—without systematically analyzing inter-stage dependencies. To address this gap, we propose the first end-to-end AI/ML-driven drug discovery framework that integrates machine learning, deep learning, natural language processing, and knowledge graph technologies, jointly modeling multi-omics and scientific literature data across stages. We validate the framework through three disease cases—hyperuricemia, gouty arthritis, and hyperuricemic nephropathy—successfully identifying novel therapeutic targets and generating viable candidate compounds. Results demonstrate significant improvements in target prediction accuracy and molecular design efficiency, while reducing early-stage attrition risk. This work establishes a foundational paradigm for cross-stage, data-integrated computational drug discovery.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Other Foundations of Human Computation & AI
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• AI integration in drug discovery to reduce costs and failure rates
• Comprehensive AI/ML review across target identification to lead optimization
• Case study on hyperuricemia diseases for practical AI impact

Innovation

• AI/ML integration in drug discovery pipeline
• Holistic analysis of target identification and optimization
• Case study on hyperuricemia and related diseases