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https://github.com/HannesStark/EquiBind

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
https://github.com/HannesStark/EquiBind

drug-discovery equivariance geometry graph-neural-networks molecules protein-structure proteins

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EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

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README 

        

# EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction



### [Paper on arXiv](https://arxiv.org/abs/2202.05146)



**Before using EquiBind, also consider checking out our new approach called DiffDock which improves over EquiBind in multiple ways.

The DiffDock [GitHub](https://github.com/gcorso/DiffDock) and [paper](https://arxiv.org/abs/2210.01776).**



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

compared to traditional and recent baselines.

 If you have questions, don't hesitate to open an issue or ask me

via [[email protected]]([email protected])

or [social media](https://hannes-stark.com/) or Octavian Ganea via [[email protected]]([email protected]). We are happy to hear from you!



![](.fig_intro.jpg)



![](.model2.jpg)



# Dataset



Our preprocessed data (see dataset section in the paper Appendix) is available from [zenodo](https://zenodo.org/record/6408497). \

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](https://zenodo.org/record/6408497) 

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`` whose names contain the string `ligand` (your ligand files should contain **all** hydrogens). \

Receptor files of the format ``.pdb`` whose names contain the string `protein`. We ran [reduce](https://github.com/rlabduke/reduce) on our training proteins. Maybe you also want to run it on your protein.\

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.mol2

...

```



## Step 2: Setup Environment



We will set up the environment using [Anaconda](https://docs.anaconda.com/anaconda/install/index.html). Clone the

current repo



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



Create a new environment with all required packages using `environment.yml`. If you have a CUDA GPU run:



    conda env create -f environment.yml



If you instead only have a CPU run:



    conda env create -f environment_cpuonly.yml



Activate the environment



    conda activate equibind



Here are the requirements themselves for the case with a CUDA GPU 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! :tada: \

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``!



# Inference for multiple ligands in the same .sdf file and a single receptor



    python multiligand_infernce.py -o path/to/output_directory -r path/to/receptor.pdb -l path/to/ligands.sdf



This runs EquiBind on every ligand in ligands.sdf against the protein in receptor.pdb. The outputs are 3 files in output_directory with the following names and contents:



failed.txt - contains the index (in the file ligands.sdf) and name of every molecule for which inference failed in a way that was caught and handled.\

success.txt - contains the index (in the file ligands.sdf) and name of every molecule for which inference succeeded.\

output.sdf - contains the conformers produced by EquiBind in .sdf format.



# 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 run``python inference.py --config=configs_clean/inference_file_for_reproduce.yml`` as above!

## Reference 



:page_with_curl: Paper [on arXiv](https://arxiv.org/abs/2202.05146)

```

@inproceedings{equibind,

  title={Equibind: Geometric deep learning for drug binding structure prediction},

  author={St{\"a}rk, Hannes and Ganea, Octavian and Pattanaik, Lagnajit and Barzilay, Regina and Jaakkola, Tommi},

  booktitle={International Conference on Machine Learning},

  pages={20503--20521},

  year={2022},

  organization={PMLR}

}

```