[1]刘晓光,李梅.基于深度学习的药物-靶标相互作用预测研究综述[J].智能系统学报,2024,19(3):494-524.[doi:10.11992/tis.202308024]
LIU Xiaoguang,LI Mei.A survey of deep learning-based drug-target interaction prediction[J].CAAI Transactions on Intelligent Systems,2024,19(3):494-524.[doi:10.11992/tis.202308024]
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LIU Xiaoguang,LI Mei.A survey of deep learning-based drug-target interaction prediction[J].CAAI Transactions on Intelligent Systems,2024,19(3):494-524.[doi:10.11992/tis.202308024]
基于深度学习的药物-靶标相互作用预测研究综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第3期
页码:
494-524
栏目:
综述
出版日期:
2024-05-05
- Title:
- A survey of deep learning-based drug-target interaction prediction
- Keywords:
- drug-target interaction; artificial intelligence; machine learning; deep learning; drug discovery and development; graph neural network; heterogeneous network; representation learning
- 分类号:
- TP18
- 文献标志码:
- 2024-04-28
- 摘要:
- 新药物研发是一项耗时、耗力、耗资的复杂工程，整体成功率低于10%。药物-靶标相互作用预测是药物筛选和药物重定位的关键环节。准确的药物-靶标相互作用预测可有效缩小候选药物分子筛选范围，加速药物研发进程。传统实验方法研究药物-靶标相互作用耗时长、成本高且伴有一定的盲目性，难以进行大规模的药物-靶标相互作用识别工作。近年来，将机器学习尤其是深度学习技术用于药物-靶标相互作用预测成为主流研究。尽管在过去10年有大量的研究工作纷纷涌现，药物-靶标相互作用预测仍然是物质密集型和长期性的工作，对研究者来说仍具有挑战性。本文梳理近年来基于深度学习的药物-靶标相互作用预测研究工作，归纳总结现有工作的研究方法、评价指标和使用的数据资源，分析现有工作的不足并提出展望。本文的研究目的是帮助药物研发领域研究者全面了解深度学习在药物-靶标相互作用预测领域的最新研究进展，从而提高研究效率和研究质量。
- Abstract:
- The development of novel drugs is a time-consuming, labor-consuming, and costly process with the overall success rate no more than 10%. The prediction of drug-target interactions (DTIs) is fundamental for drug screening and drug repositioning. Accurate DTI prediction can significantly narrow down the screening of drug candidates and accelerate the drug discovery process. The traditional experimental method for identifying DTIs is tedious and expensive and accompanied by certain blindness, which restricts it from large-scale DTI identification. Recently, applying machine learning especially deep learning techniques to DTI prediction has become the mainstream. Although a series of methods have been proposed in the last decade, DTI prediction is still a material-intensive and long-term work, and is challenging to researchers. In this survey, we review literature related to DTI prediction, and summarize the methodologies, evaluation indicators, and data sources used in these works. We also analyze the shortcomings of existing works and propose future prospects. Our motivation is to help researches dedicated to drug discovery and development to have a comprehensive understanding on the latest progress of DTI prediction so as to improve their research efficiency and research quality.
- 参考文献/References:
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备注/Memo
收稿日期:2023-08-19。
基金项目:国家自然科学基金项目(62272253, 62272252) ；中央高校基本科研专项.
作者简介:刘晓光，教授，博士，南开大学计算机学院副院长，主要研究方向为分布式系统、网络存储。主持国家和省部级科研项目 14项，获天津市教学成果奖2项。发表学术论文40余篇，E-mail：liuxg@nbjl.nankai.edu.cn;李梅，博士研究生，主要研究方向为图深度学习、知识图谱、深度学习在生物信息领域的应用。E-mail：limei-666@mail.nankai.edu.cn
通讯作者:刘晓光. E-mail：liuxg@nbjl.nankai.edu.cn
更新日期/Last Update:
1900-01-01