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

Last update: Jan 03, 2023

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.

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

Related tags

Overview

What is liGAN?

Dependencies

Usage

Owner

Matt Ragoza

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

Related tags

Overview

What is liGAN?

Dependencies

Usage

Owner

Matt Ragoza

Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCNN)

git《Joint Entity and Relation Extraction with Set Prediction Networks》(2020) GitHub:

This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams.

StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN

An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis

Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

I explore rock vs. mine prediction using a SONAR dataset

Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

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Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

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