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ReSolv

Code of the paper "Predicting solvation free energies with an implicit solvent machine learning potential".

The scripts to execute the code are the following:

  1. Train an U_vac MLP on the QM7x dataset: examples/FreeEnergyScripts/QM7x scripts/Nequip_QM7x_Prior.py

  2. Precomptue trajectories: examples/FreeEnergyScripts/QM7x scripts/TrainFreeEnergy/BAR_HFE_trajectory_generator.py
    which can be called from the bash script BAR_HFE_generate_trajectories.sh within the same directory.

  3. Train the BAR-HFE model: examples/FreeEnergyScripts/QM7x scripts/TrainFreeEnergy/BAR_HFE_training.py,
    which can be called from the bash script BAR_HFE_train.sh within the same directory.

Installation

  1. Create virtual environment with Python 3.10 and activate it
virtualenv -p /usr/bin/python3.10 venv
  1. Install all required packages: 
pip install swig, gym[box2d]
pip install -e .[all]
pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install "jax[cuda]==0.4.23" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install h5py, rdkit, scipy==1.12, imageio, networkx, dill

or use the requirements.txt file. If you use the requirements.txt, you still need to install the local chemtrain and jax with cuda support (second last line above):

1. pip install -r requirements.txt
2. pip install "jax[cuda]==0.4.23" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
3. Install chemtrain from the repository

Additional data and pre-trained models to run the code

BAR - Learn solvation free energies with ReSolv

  1. Add FreeEnergyScripts/QM7x Scripts/FreeSolvDB/database.pickle. Already added in the repository.
  2. Add FreeEnergyScripts/QM7x Scripts/TrainFreeEnergy/precomputed_trajectories/*.npy for precomputed trajectories. Already added in the repository.
  3. Add FreeEnergyScripts/QM7x Scripts/TrainFreeEnergy/checkpoints/080524_t_prod_250ps_t_equil_50ps_iL1e-06_lrd0.1_epochs500_seed7_train_389mem_0.97_epoch499.pkl for the trained U_wat model. Already added in the repository.
  4. Add FreeEnergyScripts/savedTrainers/080524_t_prod_250ps_t_equil_50ps_iL1e-06_lrd0.1_epochs500_seed7_train_389mem_0.97_epoch499.pkl for the trained U_vac model. Already added in the repository.

QM7-x Force Matching

  1. Use FreeEnergyScripts/QM7x Scripts/preprocess_qm7x.py to generate your own shuffled data.

Contact

For questions, you can reach out to s.roecken@tum.de

Citation

If you use our paper, the corresponding citation is:

@article{rocken_ReSolv,
  title={Predicting solvation free energies with an implicit solvent machine learning potential},
  author={R{\"o}cken, Sebastien and Burnet, Anton F and Zavadlav, Julija},
  journal={arXiv preprint arXiv:2406.00183},
  year={2024}
}