Create a conda environment

conda env create -f environment.yml

QuickVina2

For docking, install QuickVina 2:

wget https://github.com/QVina/qvina/raw/master/bin/qvina2.1
chmod +x qvina2.1 

MGLTools for preparing the receptor for docking (pdb -> pdbqt)

conda create -n mgltools -c bioconda mgltools

Training

Starting a new training run:

python -u train.py --config <config>.yml

Resuming a previous run:

python -u train.py --config <config>.yml --resume <checkpoint>.ckpt

Test

python test.py <checkpoint>.ckpt --test_dir <output_dir> --outdir <output_dir>

Metrics

Under the Metrics folder, verify SA, QED, Div, Time, LogP, Lipinski

QuickVina2

We follow the DiffSBDD method for verification. The verification method is as follows

First, convert all protein PDB files to PDBQT files using MGLTools

conda activate mgltools
cd analysis
python docking_py27.py <test_dir> <output_dir>
cd ..
conda deactivate

Then, compute QuickVina scores:

conda activate diff-fbdd
python analysis/docking.py --pdbqt_dir <docking_py27_outdir> --sdf_dir <test_outdir> --out_dir <qvina_outdir> --write_csv --write_dict