A deep learning method for predicting molecular properties and compound-protein interactions

doi:10.1016/j.jmgm.2022.108283

LZUFE-KMS

	A deep learning method for predicting molecular properties and compound-protein interactions
	Ma, Jun 1; Zhang, Ruisheng 1; Li, Tongfeng 1; Jiang, Jing 1; Zhao, Zhili 1; Liu, Yunwu 1; Ma, Jun 1; Ma, Jun 2; Li, Tongfeng 3
	2022-12-01
发表期刊	Journal of Molecular Graphics and Modelling
卷号	117
摘要	Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp. © 2022 Elsevier Inc.
关键词	Deep learning Forecasting Learning systems Proteins Structural design Structural properties Compound-protein interaction prediction Deep learning Interaction prediction Molecular graphs Molecular properties Molecular property prediction Property predictions Protein interaction Protein sequences Smile/protein sequence
DOI	10.1016/j.jmgm.2022.108283
收录类别	EI ; SCIE
ISSN	1093-3263
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Crystallography ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biochemistry & Molecular Biology ; Computer Science, Interdisciplinary Applications ; Crystallography ; Mathematical & Computational Biology
WOS记录号	WOS:000845016300002
出版者	Elsevier Inc.
EI入藏号	20223412606415
EI主题词	Lead compounds
EI分类号	408 Structural Design - 408.1 Structural Design, General - 461.4 Ergonomics and Human Factors Engineering - 804.1 Organic Compounds - 951 Materials Science
原始文献类型	Journal article (JA)
EISSN	1873-4243
引用统计	被引频次：4[WOS] [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/32933
专题	兰州财经大学
作者单位	1.School of Information Science and Engineering, Lanzhou University, Lanzhou; 730000, China; 2.School of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; 3.Computer College, Qinghai Normal University, Xining; 810016, China
推荐引用方式 GB/T 7714	Ma, Jun,Zhang, Ruisheng,Li, Tongfeng,et al. A deep learning method for predicting molecular properties and compound-protein interactions[J]. Journal of Molecular Graphics and Modelling,2022,117.
APA	Ma, Jun.,Zhang, Ruisheng.,Li, Tongfeng.,Jiang, Jing.,Zhao, Zhili.,...&Li, Tongfeng.(2022).A deep learning method for predicting molecular properties and compound-protein interactions.Journal of Molecular Graphics and Modelling,117.
MLA	Ma, Jun,et al."A deep learning method for predicting molecular properties and compound-protein interactions".Journal of Molecular Graphics and Modelling 117(2022).