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ChemTSv21 is a refined and extended version of ChemTS2 and MPChemTS3. The original implementations are available at tsudalab/ChemTS and yoshizoe/mp-chemts, respectively.

ChemTSv2 provides:

  • easy-to-run interface by using only a configuration file
  • easy-to-define framework for users' any reward function, molecular filter, and tree policy
  • various usage examples in the GitHub repository

Installation

Info

  • Please set up a Python 3.11 environment to use ChemTSv2.
  • OpenMPI or MPICH must be installed on your server to use ChemTSv2 with massive parallel mode.
Single process mode
pip install chemtsv2
Massive parallel mode
pip install chemtsv2[mp]

How to run ChemTSv2

Example

Clone this repository and move into it.

git clone git@github.com:molecule-generator-collection/ChemTSv2.git
cd ChemTSv2

chemtsv2 -c config/setting.yaml

mpiexec -n 4 chemtsv2-mp --config config/setting_mp.yaml

How to cite

@article{Ishida2023,
  doi = {10.1002/wcms.1680},
  url = {https://doi.org/10.1002/wcms.1680},
  year = {2023},
  month = jul,
  publisher = {Wiley},
  author = {Shoichi Ishida and Tanuj Aasawat and Masato Sumita and Michio Katouda and Tatsuya Yoshizawa and Kazuki Yoshizoe and Koji Tsuda and Kei Terayama},
  title = {ChemTSv2: Functional molecular design using de novo molecule generator},
  journal = {{WIREs} Computational Molecular Science}
}

  1. Ishida, S. and Aasawat, T. and Sumita, M. and Katouda, M. and Yoshizawa, T. and Yoshizoe, K. and Tsuda, K. and Terayama, K. (2023). ChemTSv2: Functional molecular design using de novo molecule generator. WIREs Computational Molecular Science https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1680 ↩

  2. Yang, X., Zhang, J., Yoshizoe, K., Terayama, K., & Tsuda, K. (2017). ChemTS: an efficient python library for de novo molecular generation. Science and Technology of Advanced Materials, 18(1), 972–976. https://doi.org/10.1080/14686996.2017.1401424 ↩

  3. Yang, X., Aasawat, T., & Yoshizoe, K. (2021). Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design. In International Conference on Learning Representations. https://openreview.net/forum?id=6k7VdojAIK ↩