Abstract
Drug response prediction plays a crucial role in personalized medicine and drug discovery. Many deep neural networks have been proposed for better drug response prediction. However, these methods only represent drugs as strings or represent drugs as molecular graphs, failing to capture comprehensive information about drugs. To address this challenge, we propose a joint graph and sequence representation learning model for drug response prediction, called DGSDRP. We use convolutional neural networks (CNN) to obtain local chemical context information from the drug sequences and a fusion module based on CNN and Bi-LSTM to capture the features of cell lines. Furthermore, we use graph convolutional networks (GCN) to extract topological structure information from the molecular graphs. Finally, we concatenate all representations through several dense layers and end with a regression layer to predict the response value. Extensive experimental results show that our model outperforms the current state-of-the-art models in terms of RMSE and \(CC_p\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hutchinson, L., DeVita, V.: The era of personalized medicine: back to basics. Nat. Rev. Clin. Oncol. 5, 623 (2008)
Marquart, J., Chen, E.Y., Prasad, V.: Estimation of the percentage of US patients with cancer who benefit from genome-driven oncology. Jama Oncol. 4(8), 1093–1098 (2018)
Weinstein, J.N., Collisson, E.A., Mills, G.B., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113 (2013)
Zhang, J., Baran, J., Cros, A., et al.: International cancer genome consortium data portal-a one-stop shop for cancer genomics data. Database (Oxford) Database(2011), bar026 (2011)
Yang, W., Jorge, S., Patricia, G., et al.: Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41(D1), D955–D961 (2012)
Barretina, J., Caponigro, G., Stransky, N., et al.: The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012)
Shoemaker, R.: The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6, 813–823 (2006)
Nguyen, T., Nguyen, G.T.T., Nguyen, T., Le, D.-H.: Graph convolutional networks for drug response prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 19(1), 146–154 (2022)
Liu, P., Li, H., Li, S., et al.: Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinform. 20(1), 408 (2019)
Li, M., et al.: DeepDSC: a deep learning method to predict drug sensitivity of cancer cell lines. IEEE ACM Trans. Comput. Biol. Bioinform. 18(2), 575–582 (2019)
Cortés-Ciriano, I., Bender, A.: KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images. J. Cheminform. 11(1), 1–16 (2019). https://doi.org/10.1186/s13321-019-0364-5
Ding, M.Q., Chen, L., Cooper, G.F., Young, J.D., Lu, X.: Precision oncology beyond targeted therapy: combining omics data with machine learning matches the majority of cancer cells to effective therapeutics. Mol. Cancer Res. 16(2), 269–278 (2018)
Rampášek, L., Hidru, D., Smirnov, P., Haibe-Kains, B., Goldenberg, A.: Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Bioinformatics 35(19), 3743–3751 (2019)
Wang, Y., Fang, J., Chen, S.: Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties. Sci. Rep. 6, 32679 (2016)
Rahman, R., Matlock, K., Ghosh, S., Pal, R.: Heterogeneity aware random forest for drug sensitivity prediction. Sci. Rep. 7(1), 11347 (2017)
Zhang, N., Wang, H., Fang, Y., Wang, J., Zheng, X., Liu, X.S.: Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model. PLoS Comput. Biol. 11(9), e1004498 (2015)
Li, M., et al.: GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8), 1140–1147 (2021)
Zeng, Y., Chen, X., Luo, Y., et al.: Deep drug-target binding affinity prediction with multiple attention blocks. Briefings Bioinform. 22(5), bbab117 (2021)
Gonczarek, A., Tomczak, J.M., Zareba, S., Kaczmar, J., Dabrowski, P., Walczak, M.J.: Interaction prediction in structure-based virtual screening using deep learning. Comput. Biol. Med. 100, 253–258 (2018)
Karimi, M., Wu, D., Wang, Z., Shen, Y.: DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 38(18), 3329–3338 (2019)
Özt ürk, H., Özg ür, A., Ozkirimli, E.: DeepDTA: deep drug-target binding affinity prediction. Bioinformatics 34(17), i821–i829 (2018)
Wang, L., You, Z.-H., Chen, X., Xia, S.-X., et al.: A computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J. Comput. Biol. 25(3), 361–373 (2018)
Ramsundar, B., Eastman, P., Walters, P.: Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More, vol. 1, 2nd edn. O’Reilly, New York (2019)
Kang, L., Ye, P., Li, Y., et al.: Convolutional neural networks for no-reference image quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations (ICLR 2017) (2017)
Elickovic, P.V., Cucurull, G., Casanova, A.: Graph attention networks. In: Proceedings of the International Conference on Learning Representations (ICLR 2018) (2018)
Xu, K., Hu, W., Leskovec, J.: How powerful are graph neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR 2019) (2019)
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)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61972135), and the Natural Science Foundation of Heilongjiang Province in China (No. LH2020F043).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yan, X., Liu, Y., Zhang, W. (2022). Deep Graph and Sequence Representation Learning for Drug Response Prediction. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-15919-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15918-3
Online ISBN: 978-3-031-15919-0
eBook Packages: Computer ScienceComputer Science (R0)