EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Overview

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Paper on arXiv

EquiBind, is a SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand’s bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. If you have questions, don't hesitate to open an issue or ask me via [email protected] or social media or Octavian Ganea via [email protected]. We are happy to hear from you!

Dataset

Our preprocessed data (see dataset section in the paper Appendix) is available from zenodo.
The files in data contain the names for the time-based data split.

If you want to train one of our models with the data then:

  1. download it from zenodo
  2. unzip the directory and place it into data such that you have the path data/PDBBind

Use provided model weights to predict binding structure of your own protein-ligand pairs:

Step 1: What you need as input

Ligand files of the formats .mol2 or .sdf or .pdbqt or .pdb.
Receptor files of the format .pdb
For each complex you want to predict you need a directory containing the ligand and receptor file. Like this:

my_data_folder
└───name1
    │   name1_protein.pdb
    │   name1_ligand.sdf
└───name2
    │   name2_protein.pdb
    │   name2_ligand.sdf
...

Step 2: Setup Environment

We will set up the environment using Anaconda. Clone the current repo

git clone https://github.com/HannesStark/EquiBind

Create a new environment with all required packages using environment.yml (this can take a while). While in the project directory run:

conda env create

Activate the environment

conda activate equibind

Here are the requirements themselves if you want to install them manually instead of using the environment.yml:

python=3.7
pytorch 1.10
torchvision
cudatoolkit=10.2
torchaudio
dgl-cuda10.2
rdkit
openbabel
biopython
rdkit
biopandas
pot
dgllife
joblib
pyaml
icecream
matplotlib
tensorboard

Step 3: Predict Binding Structures!

In the config file configs_clean/inference.yml set the path to your input data folder inference_path: path_to/my_data_folder.
Then run:

python inference.py --config=configs_clean/inference.yml

Done! 🎉
Your results are saved as .sdf files in the directory specified in the config file under output_directory: 'data/results/output' and as tensors at runs/flexible_self_docking/predictions_RDKitFalse.pt!

Reproducing paper numbers

Download the data and place it as described in the "Dataset" section above.

Using the provided model weights

To predict binding structures using the provided model weights run:

python inference.py --config=configs_clean/inference_file_for_reproduce.yml

This will give you the results of EquiBind-U and then those of EquiBind after running the fast ligand point cloud fitting corrections.
The numbers are a bit better than what is reported in the paper. We will put the improved numbers into the next update of the paper.

Training a model yourself and using those weights

To train the model yourself, run:

python train.py --config=configs_clean/RDKitCoords_flexible_self_docking.yml

The model weights are saved in the runs directory.
You can also start a tensorboard server tensorboard --logdir=runs and watch the model train.
To evaluate the model on the test set, change the run_dirs: entry of the config file inference_file_for_reproduce.yml to point to the directory produced in runs. Then you can runpython inference.py --config=configs_clean/inference_file_for_reproduce.yml as above!

Reference

📃 Paper on arXiv

@misc{stark2022equibind,
      title={EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction}, 
      author={Hannes Stärk and Octavian-Eugen Ganea and Lagnajit Pattanaik and Regina Barzilay and Tommi Jaakkola},
      year={2022}
}
Owner
Hannes Stärk
MIT Research Intern • Geometric DL + Graphs :heart: • M. Sc. Informatics from TU Munich
Hannes Stärk
Artificial Intelligence playing minesweeper 🤖

AI playing Minesweeper ✨ Minesweeper is a single-player puzzle video game. The objective of the game is to clear a rectangular board containing hidden

Vaibhaw 8 Oct 17, 2022
End-To-End Crowdsourcing

End-To-End Crowdsourcing Comparison of traditional crowdsourcing approaches to a state-of-the-art end-to-end crowdsourcing approach LTNet on sentiment

Andreas Koch 1 Mar 06, 2022
Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

TableParser Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at DS3 Lab 11 Dec 13, 2022

Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

PPML-TSA This repository provides all code necessary to reproduce the results reported in our paper Evaluating Privacy-Preserving Machine Learning in

Dominik 1 Mar 08, 2022
PyElastica is the Python implementation of Elastica, an open-source software for the simulation of assemblies of slender, one-dimensional structures using Cosserat Rod theory.

PyElastica PyElastica is the python implementation of Elastica: an open-source project for simulating assemblies of slender, one-dimensional structure

Gazzola Lab 105 Jan 09, 2023
Romanian Automatic Speech Recognition from the ROBIN project

RobinASR This repository contains Robin's Automatic Speech Recognition (RobinASR) for the Romanian language based on the DeepSpeech2 architecture, tog

RACAI 10 Jan 01, 2023
The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".

Block Modeling-Guided Graph Convolutional Neural Networks This repository contains the demo code of the paper: Block Modeling-Guided Graph Convolution

22 Dec 08, 2022
Implicit Deep Adaptive Design (iDAD)

Implicit Deep Adaptive Design (iDAD) This code supports the NeurIPS paper 'Implicit Deep Adaptive Design: Policy-Based Experimental Design without Lik

Desi 12 Aug 14, 2022
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

MADGRAD Optimization Method A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization pip install madgrad Try it out! A best

Meta Research 774 Dec 31, 2022
Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision. ICCV 2021.

Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision Download links and PyTorch implementation of "Towers of Ba

Blakey Wu 40 Dec 14, 2022
All of the figures and notebooks for my deep learning book, for free!

"Deep Learning - A Visual Approach" by Andrew Glassner This is the official repo for my book from No Starch Press. Ordering the book My book is called

Andrew Glassner 227 Jan 04, 2023
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 07, 2022
We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC).

EMTAUC We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC). In this code, SBGA is considered a ba

7 Nov 24, 2022
Like a cowsay but without cows!

Foxsay This is a simple program that generates pictures of a cute fox with a message. It is like a cowsay but without cows! Fox girls are better! Usag

Anastasia Kim 28 Feb 20, 2022
Multi-task head pose estimation in-the-wild

Multi-task head pose estimation in-the-wild We provide C++ code in order to replicate the head-pose experiments in our paper https://ieeexplore.ieee.o

Roberto Valle 26 Oct 06, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior. The code will release soon. Implementation Python3 PyTorch=1.0 NVIDIA GPU+

FengZhang 34 Dec 04, 2022
This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

DendroMap DendroMap is an interactive tool to explore large-scale image datasets used for machine learning. A deep understanding of your data can be v

DIV Lab 33 Dec 30, 2022
Code for the TASLP paper "PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation".

PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation Introduction Getting Started FSD50K Recipe AudioSet Recipe Label E

Yuan Gong 84 Dec 27, 2022
Official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels".

WarPI The official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels". Run python main.py --corruption_type

Haoliang Sun 3 Sep 03, 2022
A DeepStack custom model for detecting common objects in dark/night images and videos.

DeepStack_ExDark This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for d

MOSES OLAFENWA 98 Dec 24, 2022