GraphDTA: predicting drug–target binding affinity with graph neural networks

计算机科学 药物重新定位 机器学习 人工智能 Python(编程语言) 药物开发 药物发现 人工神经网络 源代码 化学信息学 药物靶点 脚本语言 药品 图形 深度学习 生物信息学 理论计算机科学 药理学 程序设计语言 生物 医学
作者
Thin Nguyen,Hang Le,Thomas P. Quinn,Tri Minh Nguyen,Thuc Duy Le,Svetha Venkatesh
出处
期刊:Bioinformatics [Oxford University Press]
卷期号:37 (8): 1140-1147 被引量:500
标识
DOI:10.1093/bioinformatics/btaa921
摘要

Abstract Summary The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug–target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of implementation The proposed models are implemented in Python. Related data, pre-trained models and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523. Supplementary information Supplementary data are available at Bioinformatics online.
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