Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks

doi:10.1016/j.engappai.2024.109472

LZUFE-KMS

	Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks
	Ma, Jun 1,2; Zhao, Zhili 1; Liu, Yunwu 1; Li, Tongfeng 1,3; Zhang, Ruisheng 1
	2025-01
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷号	139
摘要	In the field of biomedical engineering, predicting Drug-Target Affinities (DTA) is crucial. However, current affinity prediction models are predominantly manually designed, which is a complex, time-consuming process that may not effectively accommodate the diversity and complexity of datasets. To address this challenge, we propose an adaptive learning framework for predicting drug-target affinity, called Adaptive-DTA, which integrates reinforcement learning with graph neural networks to automate the design of affinity prediction models. Adaptive-DTA defines the architecture search space using directed acyclic graphs and employs an reinforcement learning algorithm to guide the architecture search, optimizing parameters based on the entropy of sampled architectures and model performance metrics. Additionally, we enhance efficiency with a two- stage training and validation strategy, incorporating low-fidelity and high-fidelity evaluations. Our framework not only alleviates the challenges associated with manual model design but also significantly improves model performance and generalization. To evaluate the performance of our method, we conducted extensive experiments on DTA benchmark datasets and compared the results with nine state-of-the-art methods. The experimental outcomes demonstrate that our proposed framework outperforms these methods, exhibiting outstanding performance in predicting drug-target affinities. Our innovative approach streamlines the design of affinity prediction model, reduces reliance on manual crafting, and enhances model generalization. Its ability to automatically optimize network architectures represents a major step forward in the automation of computational drug discovery processes.
关键词	Adaptive learning framework Deep learning Drug-target affinity Graph neural networks Reinforcement learning
DOI	10.1016/j.engappai.2024.109472
收录类别	SCIE
ISSN	0952-1976
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号	WOS:001343815500001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
原始文献类型	Article
EISSN	1873-6769
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/38146
专题	兰州财经大学
通讯作者	Ma, Jun; Zhang, Ruisheng
作者单位	1.Lanzhou Univ, Sch Informat Sci & Engn, 222 South Tianshui Rd, Lanzhou 730000, Gansu, Peoples R China; 2.Lanzhou Univ Finance & Econ, Sch Informat Engn & Artificial Intelligence, 496 Duanjiatan Rd, Lanzhou 730020, Gansu, Peoples R China; 3.Qinghai Normal Univ, Coll Comp, 38 Wusi West Rd, Xining 810008, Qinghai, Peoples R China
第一作者单位	兰州财经大学
通讯作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Ma, Jun,Zhao, Zhili,Liu, Yunwu,et al. Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2025,139.
APA	Ma, Jun,Zhao, Zhili,Liu, Yunwu,Li, Tongfeng,&Zhang, Ruisheng.(2025).Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,139.
MLA	Ma, Jun,et al."Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 139(2025).