Usage

1. Download data with download.sh
2. Process data with:
   • extract_templates.py
   • process_base.py
3. Train policy neural networks with policies.py

References

1. Segler, Marwin HS, Mike Preuss, and Mark P. Waller. "Planning chemical syntheses with deep neural networks and symbolic AI." Nature 555.7698 (2018): 604.
2. Schwaller, Philippe, et al. "“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models." Chemical science 9.28 (2018): 6091-6098.
3. Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587 (2016): 484.
4. Pascanu, Razvan, Tomas Mikolov, and Yoshua Bengio. "On the difficulty of training recurrent neural networks." International Conference on Machine Learning. 2013.
5. Coley, Connor W., et al. "Prediction of organic reaction outcomes using machine learning." ACS central science 3.5 (2017): 434-443.
6. Plehiers, Pieter P., et al. "Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics." Journal of cheminformatics 10.1 (2018): 11.

Data Sources

• Lowe, Daniel (2017): Chemical reactions from US patents (1976-Sep2016). figshare. Fileset.
• Compound building blocks are from eMolecules