Abstract
Computer-aided drug design with high performance is a promising field, and the pre-diction of drug target affinity is an important part in computer-aided drug design. As a kind of deep learning model algorithm, graph neural network series model algorithm has been gradually applied in drug and protein research due to its excellent performance in structural feature learning. In the new field of drug target affinity prediction, graph neural network also has great potential. In this paper, a novel approach for drug target affinity prediction based on multi-channel graph convolution network is proposed. The method encodes drug and protein sequences into corresponding node adjacency matrix. The adjacency matrix together with the physical and chemical characteristics of drug and protein sequences are used as the inputs of the model to construct a multi-channel graph convolution network that aggregates the information of nodes at different distances. The drug feature and target feature vectors are concatenated, and then through the full connection layer, the concatenated vector is converted to the predicted value. The experiment results on Davis dataset and KIBA dataset show that the proposed method outperforms most relevant methods. While the experimental results is Slightly worse than GraphDTA, it shows that the proposed method can improve the prediction of drug-target affinity to a certain extent and aggregate more information from other nodes of higher order proximity in the graphs.
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References
Asher, M.: New drugs cost US $2.6 billion to develop. Nat. Rev. Drug Dis. 13(12), 877 (2014)
Ashburn, T.T., Thor, K.B.: Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Disc. 3(8), 673 (2004)
Roses, A.D.: Pharmacogenetics in drug discovery and development: a translational perspective. Nat. Rev. Drug Disc. 7(10), 807 (2008)
Cohen, P.: Protein kinases–the major drug targets of the twenty-fifirst century? Nat. Rev. Drug Disc. 1(4), 309 (2002)
Noble, M.E.M., Endicott, J.A., Johnson, L.N.: Protein kinase inhibitors: insights into drug design from structure. Science 303(5665), 1800–1805 (2004)
Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans. Knowl. Data Eng. 17(8), 1036–1050 (2005)
Manning, G., Whyte, D.B., Martinez, R., Hunter, T., Sudarsanam, S.: The protein kinase complement of the human genome. Science 298(5600), 1912–1934 (2002)
Stachel, S.J., et al.: Maximizing diversity from a kinase screen: identifification of novel and selective pan-Trk inhibitors for chronic pain. J. Med. Chem. 57(13), 5800–5816 (2014)
Pahikkala, T., et al.: Toward more realistic drug–target interaction predictions. Brief. Bioinform. 16, 325–327 (2014)
He, T., Heidemeyer, M., Ban, F., Cherkasov, A., Ester, M.: SimBoost: a read-across approach for predicting drug–target binding affifinities using gradient boosting machines. J. Cheminform. 9(1), 24 (2017)
Öztürk, H., Özgür, A., Ozkirimli, E.: DeepDTA: deep drug–target binding affifinity prediction. Bioinformatics 34(17), i821–i829 (2018)
Weininger, D.: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31–36 (1988)
Öztürk, H., Ozkirimli, E., Özgür, A.: arXiv Preprint. arXiv:1902.04166 (2019)
Woźniak, M., et al.: Linguistic measures of chemical diversity and the “keywords” of molecular collections. Sci. Rep. 8, 1–10 (2018)
Sigrist, C.J.A., et al.: Prosite, a protein domain database for functional characterization and annotation. Nucleic Acids Res. 38(suppl_1), D161–D166 (2009)
Feng, Q., Dueva, E., Cherkasov, A., Ester, M.: arXiv Preprint. arXiv:1807.09741 (2018)
Nguyen, T., Le, H., Venkatesh, S.: bioRxiv, p. 684662 (2019)
Zhou, C., Shi, B., Qiu, H., Liu, J.: IEEE Access 8, 92802–92813 (2020)
Ronneberger, O., Fischer, P., Brox, T.: International Conference on Medical image Computing and Computer Assisted Intervention, pp. 234–241 (2015)
Cheol, J.J., Lin, X., Chen, X.-W.: IEEE/ACM Trans. Comput. Biol. Bioinf. 8(2), 308–315 (2010)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv:1810.00826 (2018). http://arxiv.org/abs/1810.00826
He, T., Heidemeyer, M., Ban, F., Cherkasov, A., Ester, M.: J. Cheminf. 9, 24 (2017)
Gönen, M., Heller, G.: Biometrika 92, 965–970 (2005)
Allen, D.M.: Technometrics 13, 469–475 (1971)
Benesty, J., Chen, J., Huang, Y., Cohen, I.: Noise Reduction in Speech Processing, pp. 1–4. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00296-0
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This work is supported by the National Natural Science Foundation of China (No. 61972299).
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Zhang, H., Hu, J., Zhang, X. (2022). Drug-Target Affinity Prediction Based on Multi-channel Graph Convolution. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_46
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