liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grids. It is based on libmolgrid and the gnina fork of caffe.
- numpy
- pandas
- scikit-image
- openbabel
- rdkit
- molgrid
- torch
- protobuf
- gnina version of caffe
You can use the scripts download_data.sh and download_weights.sh to download the test data
and weights that were evaluated in the above papers.
The script generate.py is used to generate atomic density grids and molecular structures from
a trained generative model.
Its basic usage can be seen in the scripts generate_vae.sh:
LIG_FILE=$1 # e.g. data/molport/0/102906000_8.sdf
python3 generate.py \
--data_model_file models/data_48_0.5_molport.model \
--gen_model_file models/vae.model \
--gen_weights_file weights/gen_e_0.1_1_disc_x_10_0.molportFULL_rand_.0.0_gen_iter_100000.caffemodel \
--rec_file data/molport/10gs_rec.pdb \
--lig_file $LIG_FILE \
--out_prefix VAE \
--n_samples 10 \
--fit_atoms \
--dkoes_make_mol \
--output_sdf \
--output_dx \
--gpu
And generate_cvae.sh:
REC_FILE=$1 # e.g. data/crossdock2020/PARP1_HUMAN_775_1012_0/2rd6_A_rec.pdb
LIG_FILE=$2 # e.g. data/crossdock2020/PARP1_HUMAN_775_1012_0/2rd6_A_rec_2rd6_78p_lig_tt_min.sdf
python3 generate.py \
--data_model_file models/data_48_0.5_crossdock.model \
--gen_model_file models/cvae.model \
--gen_weights_file weights/lessskip_crossdocked_increased_1.lowrmsd.0_gen_iter_1500000.caffemodel \
--rec_file $REC_FILE \
--lig_file $LIG_FILE \
--out_prefix CVAE \
--n_samples 10 \
--fit_atoms \
--dkoes_make_mol \
--output_sdf \
--output_dx \
--gpu
Both scripts can be run from the root directory of the repository.