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

A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.

Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Python Jupyter kernel using Poetry for reproducible notebooks

Código de um painel de auto atendimento feito em Python.

Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

Near-Optimal Sparse Allreduce for Distributed Deep Learning (published in PPoPP'22)

A system for quickly generating training data with weak supervision

AI创造营 ：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

Pip-package for trajectory benchmarking from "Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds", ECMR'21

This's an implementation of deepmind Visual Interaction Networks paper using pytorch

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the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

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Self-supervised learning optimally robust representations for domain generalization.

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