Molecular Graph Deep Sets

This repository contains an implementation of the molecular graph deep sets (MolSets) model for molecular mixture properties, associated with our paper MolSets: Molecular graph deep sets learning for mixture property modeling.

Descriptions

models.py and dmpnn.py contain implementations of MolSets with standard graph convolutions and DMPNN, respectively.

main.py, main_dmpnn.py, and predict.py are for evaluation and prediction; see Usage for details.

data_utils.py is for processing molecular graph data.

data/ provides datasets used in the paper.

Details on datasets:

• data_list.pkl contains processed data from the dataset curated in ACS Cent. Sci. 2023, 9, 2, 206–216.
  • An integer index;
  • A list of solvent molecular graphs in torch_geometric.data.Data format;
  • A list of solvent molecular weights (g/mol);
  • A list of solvent weight fractions;
  • Salt molality (mol/kg);
  • Salt molecular graph;
  • Logarithm conductivity at 298 K (log S/cm).
• data_df_stats.pkl organizes the data with some statistics in pandas.DataFrame format.
• all_bin_candidates.pkl contains the candidates (equal weight binary molecular mixture + 1 m salt) for virtual screening. Organized in the same way as data_list.pkl.

results provides model checkpoints and saves files generated in runs.

*Note: Git LFS is required to download the .pkl files properly. Please download them manually if you do not have Git LFS.

**Data handling is not yet optimized for efficiency. Contributions are welcome!

Requirements

MolSets requires the following packages:

• PyTorch >= 2.0
• PyG (torch_geometric)
• PyTorch Scatter (only for DMPNN)

The environment can be set up by running

conda env create -f environment.yml

However, there may be package compatibility issues that need manual corrections. CUDA and GPU-enabled versions of PyTorch and PyG are required to run on GPUs.

Usage

Evaluation

Use main.py to train the MolSets model (with standard graph convolutions) or evaluate it on a dataset. Set the hyperparameters in hyperpars, and the data path in dataset, then run

(screen) python main.py

and see the results. Training may take minutes to hours depending on the device and data size. For the model with DMPNN, use main_dmpnn.py instead, following similar procedures.

Inference

Use predict.py to make inferences on candidate mixtures with a trained model. Specify the path to the candidate data file in candidate_data and the model checkpoint file in model.load_. Information about training data is needed if feature normalization is used, as in data_utils.py. After setup, run

python predict.py

and the predictions will be written in a .csv file.

Citation

If you find this code useful, please consider citing the following paper:

@misc{zhang2023molsets,
      title={MolSets: Molecular graph deep sets learning for mixture property modeling}, 
      author={Hengrui Zhang and Jie Chen and James M. Rondinelli and Wei Chen},
      year={2023},
      eprint={2312.16473},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}