This is an example of the application of deep learning to Quantitative structure–activity relationship (QSAR) prediction.
The implementation is based on [1], but some minor modifications are applied. We use the PubChem dataset of chemical compounds and assay outcomes, and Chainer to build, train, and evaluate deep learning models.
See commentary.md for a detailed explanation.
$ PYTHONPATH="." python tools/train.py
The training runs on CPU by default.
If you want to run the program on GPU, add --gpu <GPU ID> option.
Run python tools/train.py --help to see the complete list of options.
Q. What is PYTHONPATH? Why do we need to specify it?
Run all tests including GPU ones.
PYTHONPATH="." nosetests tests
Without GPU tests
PYTHONPATH="." nosetests -a '!gpu' tests
[1] Dahl, G. E., Jaitly, N., & Salakhutdinov, R. (2014). Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231.