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

Related tags

Deep Learningdeep-learningprotein-structurepytorchdrug-discoverygridsgenerative-models
Overview

What is liGAN?

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.

VAE paper - 2 minute talk

CVAE paper - 15 minute talk

Dependencies

  • numpy
  • pandas
  • scikit-image
  • openbabel
  • rdkit
  • molgrid
  • torch
  • protobuf
  • gnina version of caffe

Usage

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.

Owner
Matt Ragoza
PhD student, Intelligent Systems Program, Pitt SCI
Matt Ragoza
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