Abstract
We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.
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
Dahl, G.E., Jaitly, N., Salakhutdinov, R.: Multi-task neural networks for QSAR predictions. ArXiv e-prints, June 2014
Duvenaud, D.K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., Adams, R.P.: Convolutional networks on graphs for learning molecular fingerprints. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28, pp. 2224–2232. Curran Associates Inc., Red Hook (2015)
Gawehn, E., Hiss, J.A., Schneider, G.: Deep learning in drug discovery. Mol. Inform. 35(1), 3–14 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huuskonen, J., Salo, M., Taskinen, J.: Aqueous solubility prediction of drugs based on molecular topology and neural network modeling. J. Chem. Inf. Comput. Sci. 38(3), 450–456 (1998)
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., Riley, P.: Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30(8), 595–608 (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ArXiv e-prints, December 2014
Kuhn, M., Letunic, I., Jensen, L.J., Bork, P.: The sider database of drugs and side effects. Nucleic Acids Res. 44(D1), D1075–D1079 (2016)
Landrum, G.: Rdkit: open-source cheminformatics (2006). http://www.rdkit.org. Accessed 3 Apr 2017
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lusci, A., Pollastri, G., Baldi, P.: Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model. 53(7), 1563 (2013)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30 (2013)
Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S.: Deeptox: toxicity prediction using deep learning. Front. Environ. Sci. 3, 80 (2016)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Theano Development Team: Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688, May 2016
Varnek, A., Baskin, I.: Machine learning methods for property prediction in chemoinformatics: quo vadis? J. Chem. Inf. Model. 52(6), 1413–1437 (2012)
Wallach, I., Dzamba, M., Heifets, A.: AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. ArXiv e-prints, October 2015
Weininger, D.: Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules. In: Proceedings Edinburgh Math. SOC, vol. 17, pp. 1–14 (1970)
Wu, Z., Ramsundar, B., Feinberg, E.N., Gomes, J., Geniesse, C., Pappu, A.S., Leswing, K., Pande, V.: MoleculeNet: a benchmark for molecular machine learning. ArXiv e-prints, March 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G., Boström, J. (2019). Improving the Use of Deep Convolutional Neural Networks for the Prediction of Molecular Properties. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., González, P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-319-98702-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-98702-6_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98701-9
Online ISBN: 978-3-319-98702-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)