Ensemble of Deep Learning Approaches for ATC Classification
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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.
KeywordsATC classification Deep learning Convolutional neural networks Long short-term memory networks
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.
- 7.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)Google Scholar
- 9.Lumini, A., Nanni, L.: Convolutional neural networks for ATC classification. Curr. Pharm. Des. (In Press)Google Scholar
- 17.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)Google Scholar
- 18.Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Cornell University (2014)Google Scholar
- 19.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)Google Scholar
- 20.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)Google Scholar
- 21.Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Cornell University (2014)Google Scholar
- 23.Kimura, K., Sun, L., Kudo, M.: MLC toolbox: A MATLAB/OCTAVE library for multi-label classification. ArXiv arXiv:1704.02592 (2017)