commit message

.gitattributes

@@ -0,0 +1,12 @@
+*.ipynb linguist-vendored=false
+*.ipynb linguist-detectable=false
+
+/jupyter_notebooks linguist-vendored=false
+
+jupyter_notebooks/** linguist-vendored
+
+jupyter_notebooks/** linguist-vendored=false
+
+
+jupyter_notebooks/* linguist-vendored
+jupyter_notebooks/* linguist-vendored=false

.gitignore

@@ -0,0 +1,145 @@
+renew.sh
+tmux_renew.sh
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode/
+
+
+*.zip
+
+.idea/
+
+
+#################### Project specific
+
+# this ignores everything in data except for the file
+!/data
+/data/*
+!/data/PDBBind_deepBSP_filtered/pdbbind_ids_without_overlap_with_casf.data
+!/data/timesplit_test
+!/data/timesplit_no_lig_overlap_train
+!/data/timesplit_no_lig_overlap_val
+!/data/timesplit_no_lig_or_rec_overlap_train
+!/data/timesplit_no_lig_or_rec_overlap_val
+
+
+cache
+
+logs
+
+# temporary files
+temp/
+bsub*
+stderr*
+stdout*
+
+runs2
+# this excludes everything in the runs directory except for that specific run
+!/runs
+/runs/*
+!/runs/rigid_redocking
+!/runs/flexible_self_docking

README.md

@@ -0,0 +1,123 @@
+
+# EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
+
+### [Paper on arXiv](https://arxiv.org/abs/2202.05146)
+
+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]]([email protected])
+or [social media](https://hannes-stark.com/). I am 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/6034088). \
+The files in `data` contain the names for the time-based data split. For the no-ligand overlap split described in the main paper, these are 1) train: `old_no_newL_train` 2) train: `old_no_newL_val` 3) test: `new_names` 
+
+If you want to train one of our models with the data then: 
+1. download it from [zenodo](https://zenodo.org/record/6034088) 
+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](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` (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! :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``!
+
+# 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)
+```
+@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}
+}
+```