|
|
|
| Drug-target Affinity Prediction Based on Deep Learning and Multi-layered Information Fusion |
TANG Yue-wei1,LIU Zhi-ping1,2,**( ) |
1 School of Control Science and Engineering, Shandong University, Jinan 250061, China
2 Center for Intelligent Medicine, Shandong University, Jinan 250061, China |
|
|
|
Abstract Drug discovery is a very important and costly process. Computer-assisted methods for predicting drug-protein affinity can greatly speed up the process of drug discovery. The key to the prediction of drug target affinity lies in the accurate and detailed characterization of drug and protein information. In this paper, a prediction model for drug target affinity based on deep learning and multi-layered information fusion is proposed, in an attempt to obtain better prediction performance by integrating multi-layered information of drugs and proteins. Firstly, the drug is expressed as molecular graph and ECFP, GCN module and fully connected(FC) layer are used for learning, respectively. Secondly, protein sequence and K-mer feature of protein are input into CNN module and FC layer, respectively to learn potential protein features. Finally, the features learned from the four channels are concatenated and the FC layer is used for prediction. In this study, the availability of the proposed method is verified on the two benchmark datasets of drug-targets affinity and compared with other existing models. The results show that the proposed model can obtain better prediction performance than the baseline model, which indicates that the proposed strategy for predicting drug target affinity based on multi-layered information fusion of drug and protein is effective.
|
|
Received: 16 June 2021
Published: 01 December 2021
|
|
|
|
Corresponding Authors:
Zhi-ping LIU
E-mail: zpliu@sdu.edu.cn
|
|
|
| [1] |
Mullard A. New drugs cost US$2.6 billion to develop. Nature Reviews Drug Discovery, 2014, 13(12):877.
|
|
|
| [2] |
Ashburn T T, Thor K B. Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 2004, 3(8):673-683.
pmid: 15286734
|
|
|
| [3] |
Öztürk H, Ozkirimli E, Özgür A. WideDTA: prediction of drug-target binding affinity.[2019-02-04].https://arxiv.org/abs/1902.04166 .
|
|
|
| [4] |
Yamanishi Y, Kotera M, Kanehisa M, et al. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics, 2010, 26(12):i246-i254.
doi: 10.1093/bioinformatics/btq176
|
|
|
| [5] |
Nascimento A C A, Prudêncio R B C, Costa I G. A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics, 2016, 17:46.
doi: 10.1186/s12859-016-0890-3
pmid: 26801218
|
|
|
| [6] |
Cheng Z Z, Zhou S G, Wang Y, et al. Effectively identifying compound-protein interactions by learning from positive and unlabeled examples. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 15(6):1832-1843.
doi: 10.1109/TCBB.8857
|
|
|
| [7] |
Pahikkala T, Airola A, Pietilä S, et al. Toward more realistic drug-target interaction predictions. Briefings in Bioinformatics, 2015, 16(2):325-337.
doi: 10.1093/bib/bbu010
pmid: 24723570
|
|
|
| [8] |
He T, Heidemeyer M, Ban F Q, et al. SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. Journal of Cheminformatics, 2017, 9(1):24.
doi: 10.1186/s13321-017-0209-z
|
|
|
| [9] |
Wu Y F, Gao M, Zeng M, et al. BridgeDPI: a novel graph neural network for predicting drug-protein interactions.[2021-01-29].https://arxiv.org/abs/2101.12547 .
|
|
|
| [10] |
Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine, 2012, 29(6):82-97.
|
|
|
| [11] |
Ma J S, Sheridan R P, Liaw A, et al. Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 2015, 55(2):263-274.
doi: 10.1021/ci500747n
|
|
|
| [12] |
Öztürk H, Özgür A, Ozkirimli E. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics, 2018, 34(17):i821-i829.
doi: 10.1093/bioinformatics/bty593
|
|
|
| [13] |
Abbasi K, Razzaghi P, Poso A, et al. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics, 2020, 36(17):4633-4642.
doi: 10.1093/bioinformatics/btaa544
|
|
|
| [14] |
Hirohara M, Saito Y, Koda Y, et al. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinformatics, 2018, 19(Suppl 19):526.
doi: 10.1186/s12859-018-2523-5
pmid: 30598075
|
|
|
| [15] |
Karimi M, Wu D, Wang Z Y, et al. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics, 2019, 35(18):3329-3338.
doi: 10.1093/bioinformatics/btz111
|
|
|
| [16] |
Lim J, Ryu S, Park K, et al. Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation. Journal of Chemical Information and Modeling, 2019, 59(9):3981-3988.
doi: 10.1021/acs.jcim.9b00387
|
|
|
| [17] |
Gomes J, Ramsundar B, Feinberg E N, et al. Atomic convolutional networks for predicting protein-ligand binding affinity.[2017-03-30].https://arxiv.org/abs/1703.10603 .
|
|
|
| [18] |
Rose P W, Prlić A, Altunkaya A, et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Research, 2017, 45(D1):D271-D281.
|
|
|
| [19] |
Davis M I, Hunt J P, Herrgard S, et al. Comprehensive analysis of kinase inhibitor selectivity. Nature Biotechnology, 2011, 29(11):1046-1051.
doi: 10.1038/nbt.1990
|
|
|
| [20] |
Tang J, Szwajda A, Shakyawar S, et al. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. Journal of Chemical Information and Modeling, 2014, 54(3):735-743.
doi: 10.1021/ci400709d
|
|
|
| [21] |
Rogers D, Hahn M. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 2010, 50(5):742-754.
doi: 10.1021/ci100050t
pmid: 20426451
|
|
|
| [22] |
Wu Z H, Pan S R, Chen F W, et al. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1):4-24.
doi: 10.1109/TNNLS.5962385
|
|
|
| [23] |
Ramsundar B, Eastman P, Walters P, et al. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. California: O’Reilly Media, 2019: 55-56.
|
|
|
| [24] |
Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks.[2017-02-22].https://arxiv.org/abs/1609.02907v4 .
|
|
|
| [25] |
Gönen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 2005, 92(4):965-970.
doi: 10.1093/biomet/92.4.965
|
|
|
| [26] |
Sigrist C J A, Cerutti L, de Castro E, et al. PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Research, 2010, 38(suppl_1):D161-D166.
doi: 10.1093/nar/gkp885
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
