综述图机器学习在药物靶点相互作用预测中的应用
Heterogeneous networks in drug-target interaction prediction
- 系统梳理2020-2024年基于图模型的药物靶点预测方法
- 涵盖主流数据集、评估指标与开源代码资源
- 适合药物发现与AI交叉研究者参考
药物发现耗时且成本高昂。计算型药物-靶点相互作用预测可缩小湿实验的搜索范围，从而降低研发成本。本文综述了2020至2024年间基于图机器学习的方法在该任务中的进展，涵盖整体框架、核心贡献、使用数据集及源码。文章先介绍常用数据集与性能评估指标，再分析代表性论文，并讨论未来挑战与关键待探索方向。
原文摘要 · Abstract (English)
Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can reduce these requirements by narrowing the search space for wet lab experiments. In this survey, we provide comprehensive details of graph machine learning-based methods in predicting drug-target interaction, as they have shown promising results in this field. These details include the overall framework, main contribution, datasets, and their source codes. The selected papers were mainly published from 2020 to 2024. Prior to discussing papers, we briefly introduce the datasets commonly used with these methods and measurements to assess their performance. Finally, future challenges and some crucial areas that need to be explored are discussed.
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