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Deep generative models of 3D grids for structure-based drug discovery

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liGAN

liGAN methods diagram

liGAN is a PyTorch project for structure-based drug discovery with deep generative models of atomic density grids.

Dependencies

  • numpy
  • pandas
  • scikit-image
  • torch
  • openbabel
  • rdkit
  • molgrid
  • gnina

Usage

Generating molecules

To generate molecules, you must first download the pretrained model weights:

sh download_weights.sh

Then just run the generate.py script with the default configuration file:

python3 generate.py config/generate.config

Training a model

To train a model from scratch, you must first download the full Crossdocked2020 data set:

sh download_data.sh

More info about this data set can be found here.

Then you can run the train.py script with the default configuration file:

python3 train.py config/train.config

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Deep generative models of 3D grids for structure-based drug discovery

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