Abstract
Anatomical Therapeutic Chemical (ATC) classification of unknown compounds is essential for drug development and research. In this paper, we propose a multi-label classifier system for ATC prediction based on convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNN approach extracts a 1D feature vector from the compounds utilizing information about their chemical–chemical interaction and structural and fingerprint similarities to other compounds belonging to the ATC classes. The 1D vector is then reshaped into a 2D matrix. A CNN is trained on the matrix and used to extract new features. LSTM is trained on the 1D vector and likewise used to extract features. These features are then trained on two general-purpose classifiers designed for multi-label classification, and results are fused. Rigorous experimental evaluation demonstrates the superiority of our method compared to other state-of-the-art approaches.
Keywords
- ATC classification
- Deep learning
- Convolutional neural networks
- Long short-term memory networks
This is a preview of subscription content, access via your institution.
Buying options
References
Pitts, R.C.: Reconsidering the concept of behavioral mechanisms of drug action. J. Exp. Anal. Behav. 101, 422–441 (2014)
Chen, L.: Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS ONE 7, (2012)
Dunkel, M., Günther, S., Ahmed, J., Wittig, B., Preissner, R.: SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 36, W55–W59 (2008)
Wu, L., Ai, N., Liu, Y., Fan, X.: Relating anatomical therapeutic indications by the ensemble similarity of drug sets. J. Chem. Inf. Model. 53, 2154–2160 (2013)
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27, 29–34 (1999)
Cheng, X., Zhao, S.-G., Xiao, X., Chou, K.-C.: iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 33, 341–346 (2017)
Cheng, X., Zhao, S.-G., Xiao, X., Chou, K.-C.: iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget 8, 58494–58503 (2017)
Nanni, L., Brahnam, S.: Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound. Bioinformatics 33, 2837–2841 (2017)
Lumini, A., Nanni, L.: Convolutional neural networks for ATC classification. Curr. Pharm. Des. (In Press)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015)
Nanni, L., Ghidoni, S., Brahnam, S.: Handcrafted versus non-handcrafted features for computer vision classification. Pattern Recognit 71, 158–172 (2017)
Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: Pcanet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24, 5017–5032 (2015)
Nanni, L., Ghidoni, S.: How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza? Pattern Recognit Lett 85, 1–88 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Nanni, L., Brahnam, S., Lumini, A.: Matrix representation in pattern classification. Expert Syst. Appl. 39(3), 3031–3036 (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2323 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing System, pp. 1097–1105. Curran Associates Inc, Red Hook, NY (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Cornell University (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE, Las Vegas, NV (2016)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Cornell University (2014)
Zhang, M.-L., Wu, L.: Lift: multi-label learning with label-specific features. IEEE Trans. Pattern Anal. Mach Intell 37, 107–120 (2015)
Kimura, K., Sun, L., Kudo, M.: MLC toolbox: A MATLAB/OCTAVE library for multi-label classification. ArXiv arXiv:1704.02592 (2017)
Chou, K.C.: Some remarks on predicting multi-label attributes in molecular biosystems. Mol. BioSyst. 9, 10922–11100 (2013)
Acknowledgements
We would like to acknowledge the support that NVIDIA provided us through the GPU Grant Program. We used a donated TitanX GPU to train the CNNs used in this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nanni, L., Brahnam, S., Lumini, A. (2020). Ensemble of Deep Learning Approaches for ATC Classification. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 159. Springer, Singapore. https://doi.org/10.1007/978-981-13-9282-5_12
Download citation
DOI: https://doi.org/10.1007/978-981-13-9282-5_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9281-8
Online ISBN: 978-981-13-9282-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)