longlongman/desert

Zero-Shot 3D Drug Design by Sketching and Generating (NeurIPS 2022)

DESERT

Zero-Shot 3D Drug Design by Sketching and Generating (NeurIPS 2022)

P.s. Because the project is too tied to ByteDance infrastructure, we can not sure that it can run on your device painlessly.

Requirement

Our method is powered by an old version of ParaGen (previous name ByCha).

Install it with

cd mybycha
pip install -e .
pip install horovod
pip install lightseq

You also need to install

conda install -c "conda-forge/label/cf202003" openbabel # recommend using anaconda for this project 
pip install rdkit-pypi
pip install pybel scikit-image pebble meeko==0.1.dev1 vina pytransform3d

Pre-training

Data Preparation

Our training data was extracted from the open molecule database ZINC. You need to download it first.

To get the fragment vocabulary

cd preparation
python get_fragment_vocab.py # fill blank paths in the file first

To get the training data

python get_training_data.py # fill blank paths in the file first

We also provide partial training data and vocabulary Here.

Training Shape2Mol Model

You need to fill blank paths in configs/training.yaml and train.sh.

bash train.sh

We also provide a trained checkpoint Here.

Design Molecules

Sketching

For a given protein, you need to get its pocket by using CAVITY.

Sampling molecular shapes with

cd sketch
python sketching.py # fill blank paths in the file first

Generating

bash generate.sh # fill blank paths in the file first

Citation

@inproceedings{long2022DESERT,
  title={Zero-Shot 3D Drug Design by Sketching and Generating},
  author={Long, Siyu and Zhou, Yi and Dai, Xinyu and Zhou, Hao},
  booktitle={NeurIPS},
  year={2022}
}