Deep Learning based Models for Drug-Target Interactions
DOI:
https://doi.org/10.21123/bsj.2024.9212Keywords:
Bi-GRU, Deep Learning, Drug-target interactions, Drug Discovery, MPNN, Prediction computational models., Bi-GRU, Deep Learning, Drug-target interactions, Drug Discovery, MPNN, Prediction computational models.Abstract
The typical drug development approach is slow, costly, and fraught with failure - scientists examine millions of compounds, but only a few make it to preclinical or clinical testing. Machine learning (ML), a subset of AI, is a fast-expanding subject many pharmaceutical businesses increasingly utilize. Incorporating machine learning technologies into the drug development process can aid in automating repetitive data processing and analysis processes. ML techniques may be used at several stages of drug development, including drug property prediction, drug-target interaction (DTI) prediction, and De Novo drug design. DTIs are a critical component of the drug development process. When a drug (a chemical molecule) attaches to a target (proteins or nucleic acids), it is said to bind; it alters its biological behavior/function, returning it to normal. DTI prediction is an essential part of the Drug Discovery process since it may speed up and decrease costs, but it is challenging and costly because experimental assays take a long time and are expensive. In recent years, deep learning-based approaches have demonstrated encouraging results in predicting DTI. This paper developed two deep-learning architectures to predict drug-target interactions. The first model uses message-passing neural networks (MPNN) for drug encoding and bidirectional gated recurrent units (Bi-GRU) for protein-encoding. The second model uses Bi-GRU for drug encoding and protein encoding. The two models were trained and evaluated on several benchmark datasets. Our results demonstrate that our models outperform state-of-the-art DTI prediction methods and are a promising approach for predicting DTI with high accuracy.
Received 22/06/2023
Revised 12/09/2023
Accepted 14/09/2023
Published Online First 20/04/2024
References
Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules. 2020; 25(22): 5277. https://doi.org/10.3390/molecules25225277.
Dhannoon BN. Predication and Classification of Cancer Using Sequence Alignment and Back Propagation Algorithms inBrca1 and Brca2 Genes. Int J Pharm Res 2019; 11. https://doi.org/10.31838/ijpr/2019.11.01.062.
Jassim OA, Abed MJ, Saied ZHS. Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction. Baghdad Sci J. 2023; http://dx.doi.org/10.21123/bsj.2023.8340
Duelen R, Corvelyn M, Tortorella I, Leonardi L, Chai YC, Sampaolesi M. Medicinal Biotechnology for Disease Modeling, Clinical Therapy, and Drug Discovery and Development. Int to Biotech Ent 2019;89–128: http://dx.doi.org/10.1007/978-3-030-22141-6_5
Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today. 2006; 11(13-14): 580-594. https://doi.org/10.1016/j.drudis.2006.05.015.
Dickson M, Gagnon JP. Key factors in the rising cost of new drug discovery and development. Nat Rev Drug Discov. 2004; 3(5): 417-429. https://doi.org/10.1038/nrd1389.
Kapetanovic I. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem Biol Interact. 2008; 171: 165-176. https://doi.org/10.1016/j.cbi.2007.11.011.
Sachdev K, Gupta MK. A comprehensive review of feature based methods for drug target interaction prediction. J Biomed Inform. 2019; 93: 103159. https://doi.org/10.1016/j.jbi.2019.103159.
Pliakos K, Vens C. Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. BMC Bioinform. 2020; 21: 49. https://doi.org/10.1186/s12859-020-3380-6.
Shin B, Park S, Kang K, Ho JC. Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction. Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, Ann Arbor, MI, USA, 2019. PMLR 2019; 106: 230-248. https://doi.org/10.48550/arXiv.1908.06760.
Wang L, You ZH, Xing Chen, Shi-Xiong Xia, Feng Liu, Xin Yan, et al. A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network. J Comput Biol. 2018; 25(4): 361-373. https://doi.org/10.1089/cmb.2017.0135
Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J. 2020; 18: 784-790. https://doi.org/10.1016/j.csbj.2020.03.025
Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. GraphDTA: Predicting drug-target binding affinity with graph neural networks. J. Bioinform. 2021; 37(7): 1140-1147. https://doi.org/10.1093/bioinformatics/btaa921
Abdul Raheem K. Ali, Dhannoon N. Ban, Automating Drug Discovery using Machine Learning, Curr Drug Discov Technol 2023; 20 : e070623217776. https://dx.doi.org/10.2174/1570163820666230607163313
Ye Q, Zhang X, Lin X. Drug-target interaction prediction via multiple classification strategies. BMC Bioinform 2022 Jan 20; 22(Suppl 12): 461. https://doi.org/10.1186/s12859-021-04366-3
de Souza JG, Fernandes MAC, de Melo Barbosa R. A Novel Deep Neural Network Technique for Drug-Target Interaction. Pharmaceutics. 2022; 14(3): 625. https://doi.org/10.3390/pharmaceutics14030625.
Shim J, Hong ZY, Sohn I, Hwang C. Prediction of drug-target binding affinity using similarity-based convolutional neural network. Sci Rep. 2021; 11(1): 4416. https://doi.org/10.1038/s41598-021-83679-y
Lee I, Nam H. Sequence-based prediction of protein binding regions and drug-target interactions. J Cheminform. 2022;14(1):5. https://doi.org/10.1186/s13321-022-00584-w
Öztürk H, Özgür A, Ozkirimli E. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018; 34(17): i821-i829. https://doi.org/10.1093/bioinformatics/bty593
Mukherjee S, Ghosh M, Basuchowdhuri P. Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity. Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). 2022 ;729–37. http://dx.doi.org/10.1137/1.9781611977172.82
Shao K, Zhang Z, He S, Bo XC. DTIGCCN: Prediction of drug-target interactions based on GCN and CNN. EEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) 2020. https://doi.org/10.1109/ictai50040.2020.00060.
Tsubaki M, Tomii K, Sese J. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. J. Bioinform. 2019; 35(2): 309-318. https://doi.org/10.1093/bioinformatics/bty535.
Ranjan A, Shukla S, Datta D, Misra R. Generating novel molecule for target protein (SARS-CoV-2) using drug-target interaction based on graph neural network. Netw Model Anal Health Inform Bioinform. 2022; 11(1): 6. https://doi.org/10.1007/s13721-021-00351-1
Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, et al . Deep-Learning-Based Drug–Target Interaction Prediction. J Proteome Res. 2017; 16(4): 1401-1409. https://doi.org/10.1021/acs.jproteome.6b00618.s001
Wen T, Altman RB. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. J Chem Inf Model. 2019; 59(10): 4131-4149. https://doi.org/10.1021/acs.jcim.9b00628
Zhao T, Hu Y, Valsdottir LR, Zang T, Peng J. Identifying drug-target interactions based on graph convolutional network and deep neural network. Brief Bioinform. https://doi.org/10.1093/bib/bbaa044
Davis M.I, Hunt J.P, Herrgard S, Ciceri P, Wodicka L.M, Pallares G, et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol. 2011(29): 1046–1051. https://doi.org/10.1038/nbt.1990.
Wotaifi Tahseen, Dhannoon Ban. An Effective Hybrid Deep Neural Network for Arabic Fake News Detection. Baghdad Sci J. 2023, 20. https://doi.org/10.21123/bsj.2023.7427
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