This repository is the official implementation of Hosseini, Simini, Clyde, and Ramanathan's Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks.

To install requirements:

pip install -r requirements.txt

📋 Note: Pytorch geometric may require a seperate installation process, depending on the system being used. If Pytorch Geometric cannot be installed using the requirements file, refer to the Pytorch Geometric Installation Guide to install Pytorch Geometric, and then use the requirements file to install all other dependencies.

The ZINC dataset subset used in this project, along with the docking scores obtained by Lyu et al (2019), can be downloaded directly from the authors here. The default settings in graph-dock/config.json expect this data to have the following path ./data/d4_table_name_smi_energy_hac_lte_25_title.csv. However, this can easily be modified in the configuration file. 

Before running any training or inference, this data needs to be preprocessed using the preprocess_data function in util.py. Invoking util.py as a script in the root directory of the repository will create a preprocessed version of the data consistent with current configuration settings in graph-dock/config.json. All other (in memory) preprocessing is handled automatically by the training script.

To train the model(s) in the paper, modify the configuration file ./graph-dock/config.json as needed and run this command:

python train.py

The provided ./graph-dock/config.json file contains the default hyperparameters used to obtain the FiLMv2 results in our work.

To evaluate a model on a subset of the ZINC dataset, run:

python inference.py 

Note that this script also depends on the ./graph-dock/config.json file, which currently contains the default parameters used to obtain the results in our work.

Our model achieves the following performance on a witheld test partition of the ZINC dataset:

Please refer to our paper for more details.

We greatly welcome suggestions and contributions to our code! Please feel free to fork this repository, hack away, and submit a pull request.

If you find our work useful, please cite our work using the following BibTeX:

@inproceedings{deep_surrogate_dock,
  author    = {Hosseini, Ryien and Simini, Filippo and Clyde, Austin and Ramanathan, Arvind},
  title     = {Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
},
  year      = {2022},
  maintitle = {Advances in Neural Information Processing Systems 35 (2022)},
  booktitle = {Workshop on AI for Science: Progress and Promises},
}