• Installation
• Getting started
• Citation

This package implements the PQR framework -- a generative approach to structure-based ligand elaboration. The framework consists of a multi-level contrastive learning protocol that constructs a generative posterior as a product of context factors, representing 1D, 2D and 3D context information. A description of the method can be found here.

This particular implementation uses stochastic reconstructions during model training, with the 2D and 3D context factors represented by graph-convolutional and hypergraph-convolutional neural networks, respectively.

The code has been tested on 64-bit Linux only. GPU support is essential for model training, with a recommended GPU RAM of at least 16GB.

1. Set up a new python3 conda environment
2. Install pytorch
3. Install pytorch-geometric
4. Install rdkit
5. Install benchml
6. Clone and install libpqr

An example installation script looks like this:

conda create --prefix "./venv" python=3.8
conda activate ./venv
pip3 install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio==0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip3 install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.8.1+cu102.html
pip3 install rdkit-pypi
pip3 install benchml
git clone https://github.com/capoe/libpqr.git
cd libpqr
pip install .

The pretrained models provided in the ./models directory have been obtained with the following configuration:

torch==1.8.2+cu102
torch-cluster==1.6.0
torch-geometric==2.0.4
torch-scatter==2.0.9
torch-sparse==0.6.12
rdkit-pypi==2021.9.5.1
libpqr==0.1.0

The workflow is described here. The instructions will guide you through the individual training steps. Note that, to build performant models, you will first need to download and preprocess the complete datasets.

A description of the framework is available on arXiv -- please cite this if you find the method and/or code useful:

@article{chan_3d_2022,
  doi = {10.48550/ARXIV.2204.10663},
  url = {https://arxiv.org/abs/2204.10663},
  author = {Chan, Lucian and Kumar, Rajendra and Verdonk, Marcel and Poelking, Carl},
  keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), Biomolecules (q-bio.BM)},
  title = {3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design},
  publisher = {arXiv},
  year = {2022}
}