https://github.com/divelab/graphbp

Official implementation of "Generating 3D Molecules for Target Protein Binding" [ICML2022 Long Presentation]
https://github.com/divelab/graphbp

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Official implementation of "Generating 3D Molecules for Target Protein Binding" [ICML2022 Long Presentation]

• Host: GitHub
• URL: https://github.com/divelab/graphbp
• Owner: divelab
• License: gpl-3.0
• Created: 2022-04-18T19:22:25.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-07-20T16:35:44.000Z (over 1 year ago)
• Last Synced: 2023-07-20T17:37:54.814Z (over 1 year ago)
• Language: Python
• Homepage:
• Size: 8.83 MB
• Stars: 84
• Watchers: 4
• Forks: 22
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Update: Our code has been moved into [AIRS](https://github.com/divelab/AIRS/tree/main/OpenMI/GraphBP). Please refer to AIRS for any future updates. This repo is no longer maintained.

# Generating 3D Molecules for Target Protein Binding

This is the official implementation of the **GraphBP** method proposed in the following paper.

Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji. "[Generating 3D Molecules for Target Protein Binding](https://arxiv.org/abs/2204.09410)". [ICML 2022 **Long Presentation**]

![](https://github.com/divelab/GraphBP/blob/main/assets/GraphBP.png)

## Requirements

We include key dependencies below. The versions we used are in the parentheses. Our detailed environmental setup is available in [environment.yml](https://github.com/divelab/GraphBP/blob/main/GraphBP/environment.yml).

* PyTorch (1.9.0)

* PyTorch Geometric (1.7.2)

* rdkit-pypi (2021.9.3)

* biopython (1.79)

* openbabel (3.3.1)

## Preparing Data

* Download and extract the CrossDocked2020 dataset:

```linux

wget https://bits.csb.pitt.edu/files/crossdock2020/v1.1/CrossDocked2020_v1.1.tgz -P data/crossdock2020/

tar -C data/crossdock2020/ -xzf data/crossdock2020/CrossDocked2020_v1.1.tgz

wget https://bits.csb.pitt.edu/files/it2_tt_0_lowrmsd_mols_train0_fixed.types -P data/crossdock2020/

wget https://bits.csb.pitt.edu/files/it2_tt_0_lowrmsd_mols_test0_fixed.types -P data/crossdock2020/

```

**Note**: (1) The unzipping process could take a lot of time. Unzipping on SSD is much faster!!! (2) Several samples in the training set cannot be processed by our code. Hence, we recommend replacing the `it2_tt_0_lowrmsd_mols_train0_fixed.types` 

file with a new one, where these samples are deleted. The new one is available [here](https://github.com/divelab/GraphBP/blob/main/GraphBP/data/crossdock2020/it2_tt_0_lowrmsd_mols_train0_fixed.types).

* Split data files:

```linux

python scripts/split_sdf.py data/crossdock2020/it2_tt_0_lowrmsd_mols_train0_fixed.types data/crossdock2020

python scripts/split_sdf.py data/crossdock2020/it2_tt_0_lowrmsd_mols_test0_fixed.types data/crossdock2020

```

## Run

* Train GraphBP from scratch:

```linux

CUDA_VISIBLE_DEVICES=${you_gpu_id} python main.py

```

**Note**: GraphBP can be trained on a `48GB GPU` with `batchsize=16`. Our trained model is available [here](https://github.com/divelab/GraphBP/blob/main/GraphBP/trained_model/model_33.pth).

* Generate atoms in the 3D space with the trained model:

```linux

CUDA_VISIBLE_DEVICES=${you_gpu_id} python main_gen.py

```

* Postprocess and then save the generated molecules:

```linux

CUDA_VISIBLE_DEVICES=${you_gpu_id} python main_eval.py

```

## Reference

```

@inproceedings{liu2022graphbp,

  title={Generating 3D Molecules for Target Protein Binding},

  author={Meng Liu and Youzhi Luo and Kanji Uchino and Koji Maruhashi and Shuiwang Ji},

  booktitle={International Conference on Machine Learning},

  year={2022}

}

```

## Acknowledgments

This work was supported in part by National Science Foundation grants IIS-2006861 and IIS-1908220.