https://github.com/luost26/3D-Generative-SBDD

💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)
https://github.com/luost26/3D-Generative-SBDD

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💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)

• Host: GitHub
• URL: https://github.com/luost26/3D-Generative-SBDD
• Owner: luost26
• License: mit
• Created: 2021-10-25T11:57:45.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-02-12T03:41:05.000Z (almost 2 years ago)
• Last Synced: 2024-11-18T01:05:34.325Z (about 1 month ago)
• Language: Python
• Homepage:
• Size: 4.59 MB
• Stars: 183
• Watchers: 15
• Forks: 43
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# A 3D Generative Model for Structure-Based Drug Design



[[Paper](https://arxiv.org/abs/2203.10446)]

[[Slides](https://drive.google.com/file/d/1PnypgrWqZAnaONy_Ccq-fmTDo4IRJpX1/view?usp=sharing)]

## 📢 News

- Please check out our latest work on structure-based drug design: *Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets* (ICML 2022)

  - Code: https://github.com/pengxingang/Pocket2Mol

  - Paper: https://arxiv.org/abs/2205.07249

## Installation

### Dependency

The code has been tested in the following environment:

| Package           | Version   |

|-|-|

| Python            | 3.8.12    |

| PyTorch           | 1.10.1    |

| CUDA              | 11.3.1    |

| PyTorch Geometric | 2.0.3     |

| RDKit             | 2020.09.5 |

| OpenBabel         | 3.1.0     |

| BioPython         | 1.79      |

### Install via Conda YML FIle (CUDA 11.3)

```bash

conda env create -f env_cuda113.yml

conda activate SBDD-3D

```

### Install Manually

```bash

conda create --name SBDD-3D python=3.8

conda activate SBDD-3D

conda install pytorch=1.10.1 torchvision torchaudio cudatoolkit=11.3 -c pytorch

conda install pyg -c pyg -c conda-forge

conda install easydict -c conda-forge

conda install biopython -c conda-forge

conda install rdkit openbabel python-lmdb -c conda-forge

conda install tensorboard -c conda-forge

```

## Datasets

Please refer to [`README.md`](./data/README.md) in the `data` folder.

## Sampling

### Sampling for Pockets in the Testset

To sample molecules for the i-th pocket in the testset, please first download the trained models following 

[`README.md`](./pretrained/README.md) in the `pretrained` folder. Then, run the following command:

```bash

python sample.py ./configs/sample.yml --data_id {i}  # Replace {i} with the index of the data. i is between 0 and 99 for the testset.

```

### Sampling for PDB Structures

To generate ligands for your own pocket, you need to provide the `PDB` structure file of the protein, the center coordinate of the pocket bounding box, and optionally the side length of the bounding box (default: 22Å).

Example:

```bash

python sample_for_pdb.py \

    --pdb_path ./example/4yhj.pdb \

    --center 32.0,28.0,36.0

```



## Training

The open source repo of our latest work [Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets](https://arxiv.org/abs/2205.07249) (ICML 2022) is tested for training. 

You may check it out here: https://github.com/pengxingang/Pocket2Mol

## Citation

```

@inproceedings{luo2021sbdd,

    title={A 3D Generative Model for Structure-Based Drug Design},

    author={Shitong Luo and Jiaqi Guan and Jianzhu Ma and Jian Peng},

    booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},

    year={2021}

}

```