综述图神经网络在药物发现中的应用与挑战。
A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
- 系统梳理图神经网络在药物发现各环节的应用思路。
- 覆盖分子性质预测、药物重定位等六大研究方向。
- 适合关注AI制药的科研人员和从业者参考。
图神经网络（GNN）因其处理药物分子等图结构数据的能力，在复杂的药物发现领域受到广泛关注。已有大量研究涵盖多个药物发现方向，包括分子性质预测（如药物-靶点结合亲和力预测）、药物-药物相互作用研究、微生物组相互作用预测、药物重定位、逆合成分析及新药设计。本文全面综述了近年来相关研究成果，并为未来GNN在药物发现中的应用提供指导。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug interaction study, microbiome interaction prediction, drug repositioning, retrosynthesis, and new drug design, and provides guidance for future work on GNNs for drug discovery.
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