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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks

Author(s): Qingyu Tian*, Mao Ding*, Hui Yang, Caibin Yue, Yue Zhong, Zhenzhen Du, Dayan Liu, Jiali Liu and Yufeng Deng

Volume 25, Issue 4, 2022

Published on: 15 February, 2021

Page: [634 - 641] Pages: 8

DOI: 10.2174/1386207324666210215101825

Price: $65

Abstract

Background: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance

Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output.

Result: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA

Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Keywords: Drug repurposing, drug-target affinity, SMILES, BiGRU, LSTM, proteins.

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[1]
Failli, M.; Paananen, J.; Fortino, V. Prioritizing target-disease associations with novel safety and efficacy scoring methods. Sci. Rep., 2019, 9(1), 9852.
[http://dx.doi.org/10.1038/s41598-019-46293-7] [PMID: 31285471]
[2]
Jain, K.; Kumar, A. An optimal RSSI-based cluster-head selection for sensor networks. International Journal of Adaptive and Innovative Systems, 2019, 2(4), 349-361.
[http://dx.doi.org/10.1504/IJAIS.2019.108428]
[3]
Zhao, Y.; Liu, X.; Sun, W. A chain membrane model with application in cluster analysis. International Journal of Adaptive and Innovative Systems, 2019, 2(4), 324-348.
[http://dx.doi.org/10.1504/IJAIS.2019.108417]
[4]
Song, T.; Rodríguez-Patón, A.; Zheng, P. Spiking neural P systems with colored spikes. IEEE Transactions on Cognitive and Developmental Systems, 2017, 10(4), 1106-1115.
[http://dx.doi.org/10.1109/TCDS.2017.2785332]
[5]
Napolitano, F.; Zhao, Y.; Moreira, V.M.; Tagliaferri, R.; Kere, J.; D’Amato, M.; Greco, D. Drug repositioning: a machine-learning approach through data integration. J. Cheminform., 2013, 5(1), 30.
[http://dx.doi.org/10.1186/1758-2946-5-30] [PMID: 23800010]
[6]
Cao, Y.; Jiang, T.; Girke, T. A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics, 2008, 24(13), i366-i374.
[http://dx.doi.org/10.1093/bioinformatics/btn186] [PMID: 18586736]
[7]
Deshpande, M.; Kuramochi, M.; Wale, N. Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans. Knowl. Data Eng., 2005, 17(8), 1036-1050.
[http://dx.doi.org/10.1109/TKDE.2005.127]
[8]
Manning, G.; Whyte, D.B.; Martinez, R.; Hunter, T.; Sudarsanam, S. The protein kinase complement of the human genome. Science, 2002, 298(5600), 1912-1934.
[http://dx.doi.org/10.1126/science.1075762] [PMID: 12471243]
[9]
Stachel, S.J.; Sanders, J.M.; Henze, D.A.; Rudd, M.T.; Su, H.P.; Li, Y.; Nanda, K.K.; Egbertson, M.S.; Manley, P.J.; Jones, K.L.; Brnardic, E.J.; Green, A.; Grobler, J.A.; Hanney, B.; Leitl, M.; Lai, M.T.; Munshi, V.; Murphy, D.; Rickert, K.; Riley, D.; Krasowska-Zoladek, A.; Daley, C.; Zuck, P.; Kane, S.A.; Bilodeau, M.T. Maximizing diversity from a kinase screen: identification of novel and selective pan-Trk inhibitors for chronic pain. J. Med. Chem., 2014, 57(13), 5800-5816.
[http://dx.doi.org/10.1021/jm5006429] [PMID: 24914455]
[10]
Liu, K.; Wang, B. Designing DNA code: quantity and quality. International Journal of Adaptive and Innovative Systems, 2019, 2(4), 298-323.
[http://dx.doi.org/10.1504/IJAIS.2019.108402]
[11]
Kansal, S.; Bansod, P.P.; Kumar, A. Prediction of instantaneous heart rate using adaptive algorithms. International Journal of Adaptive and Innovative Systems, 2019, 2(4), 267-281.
[http://dx.doi.org/10.1504/IJAIS.2019.108397]
[12]
Öztürk, H.; Özgür, A.; Ozkirimli, E. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics, 2018, 34(17), i821-i829.
[http://dx.doi.org/10.1093/bioinformatics/bty593] [PMID: 30423097]
[13]
Öztürk, H.; Ozkirimli, E.; Özgür, A. WideDTA: prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166, 2019.
[14]
Woźniak, M.; Wołos, A.; Modrzyk, U.; Górski, R.L.; Winkowski, J.; Bajczyk, M.; Szymkuć, S.; Grzybowski, B.A.; Eder, M. Linguistic measures of chemical diversity and the “keywords” of molecular collections. Sci. Rep., 2018, 8(1), 7598.
[http://dx.doi.org/10.1038/s41598-018-25440-6] [PMID: 29765058]
[15]
Sigrist, C.J.A.; Cerutti, L.; De Castro, E. PROSITE, a protein domain database for functional characterization and annotation. Nucleic acids research, 2010, 38(1), D161-D166.
[http://dx.doi.org/10.1093/nar/gkp885]
[16]
Nguyen, T.; Le, H.; Venkatesh, S. GraphDTA: prediction of drug–target binding affinity using graph convolutional networks. bioRxiv, 2019, 684662.
[17]
Abbasi, K.; Razzaghi, P.; Poso, A.; Amanlou, M.; Ghasemi, J.B.; Masoudi-Nejad, A. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics, 2020, 36(17), 4633-4642.
[http://dx.doi.org/10.1093/bioinformatics/btaa544] [PMID: 32462178]
[18]
Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput., 1997, 9(8), 1735-1780.
[http://dx.doi.org/10.1162/neco.1997.9.8.1735] [PMID: 9377276]
[19]
Lin, X.; Zhao, K.; Xiao, T. DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction. arXiv preprint arXiv:2003.13902, 2020.
[20]
Barbuzzi, D.; Massaro, A.; Galiano, A. Multi-domain intelligent system for document image retrieval. International Journal of Adaptive and Innovative Systems, 2019, 2(4), 282-297.
[http://dx.doi.org/10.1504/IJAIS.2019.108381]
[21]
Song, T.; Zeng, X.; Zheng, P.; Jiang, M.; Rodriguez-Paton, A. A parallel workflow pattern modeling using spiking neural P systems with colored spikes. IEEE Trans. Nanobiosci., 2018, 17(4), 474-484.
[http://dx.doi.org/10.1109/TNB.2018.2873221] [PMID: 30281471]
[22]
Song, T.; Pan, L.; Wu, T.; Zheng, P.; Wong, M.L.D.; Rodriguez-Paton, A. Spiking neural P systems with learning functions. IEEE Trans. Nanobiosci., 2019, 18(2), 176-190.
[http://dx.doi.org/10.1109/TNB.2019.2896981] [PMID: 30716044]
[23]
Pham, H.V.; Moore, P.; My, L.N.T. A knowledge-based consultancy system using ICT Newhouse indicators with reasoning techniques for consultants in e-learning. International Journal of Adaptive and Innovative Systems, 2015, 2(3), 254-266.
[http://dx.doi.org/10.1504/IJAIS.2015.074410]
[24]
Song, T.; Wang, X.; Li, X. A programming triangular DNA origami for doxorubicin loading and delivering to target ovarian cancer cells. Oncotarget, 2017, 5.
[http://dx.doi.org/10.18632/oncotarget.23733]
[25]
Song, T.; Zheng, P.; Wong, M.L.D. Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Inf. Sci., 2016, 372, 380-391.
[http://dx.doi.org/10.1016/j.ins.2016.08.055]
[26]
Chung, J.; Gulcehre, C.; Cho, K. Gated feedback recurrent neural networks. International conference on machine learning, 2015.
[27]
Wang, X.; Zheng, P.; Song, T. Computing with bacteria conjugation: Small universal systems. Moleculer, 2018, 23(6), 1307.
[http://dx.doi.org/10.3390/molecules23061307]
[28]
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci., 1988, 28(1), 31-36.
[http://dx.doi.org/10.1021/ci00057a005]
[29]
Gong, F.; Yue, H.; Song, T. Discriminative Correlation Filter for Long-Time Tracking. Comput. J., 2020, 63(3), 460-468.
[http://dx.doi.org/10.1093/comjnl/bxz049]
[30]
Pang, S.; Qiao, S.; Song, T. An improved convolutional network architecture based on residual modeling for person re-identification in edge computing. IEEE Access, 2019, 7, 106749-106760.
[http://dx.doi.org/10.1109/ACCESS.2019.2933364]
[31]
Song, T.; Jiang, J.; Li, W. A deep learning method with merged LSTM Neural Networks for SSHA Prediction. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2020, 13, 2853-2860.
[http://dx.doi.org/10.1109/JSTARS.2020.2998461]
[32]
Song, T.; Meng, F.; Rodriguez-Paton, A. U-Next: A novel convolution neural network with an aggregation U-Net architecture for gallstone segmentation in CT images. IEEE Access, 2019, 7, 166823-166832.
[http://dx.doi.org/10.1109/ACCESS.2019.2953934]
[33]
Quan, Z.; Lin, X.; Wang, Z.J. A system for learning atoms based on long short-term memory recurrent neural networks. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, pp. 728-733.
[http://dx.doi.org/10.1109/BIBM.2018.8621313]
[34]
Mikolov, T.; Sutskever, I.; Chen, K. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst., 2013, 3111-3119.
[35]
Song, T.; Pang, S.; Hao, S. A parallel image skeletonizing method using spiking neural P systems with weights. Neural Process. Lett., 2019, 50(2), 1485-1502.
[http://dx.doi.org/10.1007/s11063-018-9947-9]
[36]
Gong, F; Li, C; Tao, S. A real-time fire detection method from video with multifeature fusion. Computational intelligence and neuroscience, 2019, 2019
[http://dx.doi.org/10.1155/2019/1939171]
[37]
Ramsundar, B.; Eastman, P.; Walters, P. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more; O'Reilly Media, Inc, 2019.
[38]
Landrum, G. RDKit: Open-source cheminformatics. 2006.
[39]
Song, T.; Wang, Z.; Xie, P. A novel dual path gated recurrent unit model for sea surface salinity prediction. J. Atmos. Ocean. Technol., 2020, 37(2), 317-325.
[http://dx.doi.org/10.1175/JTECH-D-19-0168.1]
[40]
Shi, X.; Wang, Z.; Deng, C.; Song, T.; Pan, L.; Chen, Z. A novel bio-sensor based on DNA strand displacement. PLoS One, 2014, 9(10), e108856.
[http://dx.doi.org/10.1371/journal.pone.0108856] [PMID: 25303242]
[41]
Yuan, S.; Deng, G.; Feng, Q. Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy-aware Scheduling in Heterogeneous Computing Systems. J. UCS, 2017, 23(7), 636-651.
[42]
Kipf, T.N.; Welling, M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[43]
Veličković, P.; Cucurull, G.; Casanova, A. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
[44]
Xu, K.; Hu, W.; Leskovec, J. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.
[45]
Davis, M.I.; Hunt, J.P.; Herrgard, S.; Ciceri, P.; Wodicka, L.M.; Pallares, G.; Hocker, M.; Treiber, D.K.; Zarrinkar, P.P. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol., 2011, 29(11), 1046-1051.
[http://dx.doi.org/10.1038/nbt.1990] [PMID: 22037378]
[46]
Tang, J.; Szwajda, A.; Shakyawar, S.; Xu, T.; Hintsanen, P.; Wennerberg, K.; Aittokallio, T. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J. Chem. Inf. Model., 2014, 54(3), 735-743.
[http://dx.doi.org/10.1021/ci400709d] [PMID: 24521231]

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