Learned Feature Generation for Molecules
When classifying molecules for virtual screening, the molecular structure first needs to be converted into meaningful features, before a classifier can be trained. The most common methods use a static algorithm that has been created based on domain knowledge to perform this generation of features. We propose an approach where this conversion is learned by a convolutional neural network finding features that are useful for the task at hand based on the available data. Preliminary results indicate that our current approach can already come up with features that perform similarly well as common methods. Since this approach does not yet use any chemical properties, results could be improved in future versions.
KeywordsConvolutional neural networks Feature generation Molecular features Virtual screening
This work was partially funded by the Konstanz Research School Chemical Biology and KNIME AG.
- 1.ChEMBL. https://www.ebi.ac.uk/chembl/
- 2.Deepchem. https://deepchem.io/
- 3.DUD - A Directory of Useful Decoys. http://dud.docking.org/
- 12.Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
- 13.Landrum, G.A., et al.: RDKit: Open-source cheminformatics. https://www.rdkit.org/ (2006)
- 14.Le Cun, Y., et al.: Handwritten zip code recognition with multilayer networks. In: Proceedings. 10th International Conference on Pattern Recognition, 1990, vol. 2, pp. 35–40. IEEE (1990)Google Scholar
- 17.Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V.: Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072 (2015)
- 21.Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
- 23.Todeschini, R., Consonni, V.: Handbook of Molecular Descriptors, vol. 11. Wiley, New York (2008)Google Scholar
- 24.Unterthiner, T., et al.: Deep learning as an opportunity in virtual screening. Proc. Deep Learn. Workshop NIPS 27, 1–9 (2014)Google Scholar
- 25.Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)Google Scholar