misato-dataset 0.0.6

UNOFFICIAL Misato dataset pypi package. For instructions on dataset download see official GitHub page (https://github.com/t7morgen/misato-dataset).

MISATO - Machine learning dataset of protein-ligand complexes for structure-based drug discovery

:earth_americas: Where we are:

• Quantum Mechanics: 19443 ligands, curated and refined
• Molecular Dynamics: 16972 simulated protein-ligand structures, 10 ns each
• AI: pytorch dataloaders, 3 base line models for MD and QM and binding affinity prediction

:electron: Vision:

We are a drug discovery community project :hugs:

• highest possible accuracy for ligand molecules
• represent the systems dynamics in reasonable timescales
• innovative AI models for drug discovery predictions

Lets crack the 100+ ns MD, 30000+ protein-ligand structures and a whole new world of AI models for drug discovery together.

Check out the paper!

:purple_heart: Community

Want to get hands-on for drug discovery using AI?

Join our discord server!

Check out our Hugging Face spaces to run and visualize the adaptability model and to perform QM property predictions.

📌  Introduction

In this repository, we show how to download and apply the Misato database for AI models. You can access the calculated properties of different protein-ligand structures and use them for training in Pytorch based dataloaders. We provide a small sample of the dataset along with the repo.

You can freely download the FULL MISATO dataset from Zenodo using the links below:

• MD (133 GiB)
• QM (0.3 GiB)
• electronic densities (6 GiB)
• MD restart and topology files (55 GiB)

wget -O data/MD/h5_files/MD.hdf5 https://zenodo.org/record/7711953/files/MD.hdf5
wget -O data/QM/h5_files/QM.hdf5 https://zenodo.org/record/7711953/files/QM.hdf5

Start with the notebook src/getting_started.ipynb to :

• Understand the structure of our dataset and how to access each molecule's properties.
• Load the PyTorch Dataloaders of each dataset.
• Load the PyTorch lightning Datamodules of each dataset.

🚀  Quickstart

We recommend to pull our MISATO image from DockerHub or to create your own image (see docker/). The images use cuda version 11.8. We recommend to install on your own system a version of CUDA that is a least 11.8 to ensure that the drivers work correctly.

# clone project
git clone https://github.com/t7morgen/misato-dataset.git
cd misato-dataset

For singularity use:

# get the container image
singularity pull docker://sab148/misato-dataset
singularity shell misato.sif

For docker use:

sudo docker pull sab148/misato-dataset:latest
bash docker/run_bash_in_container.sh

Project Structure

├── data                   <- Project data
│   ├──MD 
│   │   ├── h5_files           <- storage of dataset
│   │   └── splits             <- train, val, test splits
│   └──QM
│   │   ├── h5_files           <- storage of dataset
│   │   └── splits             <- train, val, test splits
│
├── src                    <- Source code
│   ├── data                    
│   │   ├── components           <- Datasets and transforms
│   │   ├── md_datamodule.py     <- MD Lightning data module
│   │   ├── qm_datamodule.py     <- QM Lightning data module
│   │   │
│   │   └── processing           <- Skripts for preprocessing, inference and conversion
│   │      ├──...    
│   ├── getting_started.ipynb     <- notebook : how to load data and interact with it
│   └── inference.ipynb           <- notebook how to run inference
│
├── docker                    <- Dockerfile and execution script 
└── README.md

Installation using your own conda environment

In case you want to use conda for your own installation please create a new misato environment.

In order to install pytorch geometric we recommend to use pip (within conda) for installation and to follow the official installation instructions:pytorch-geometric/install

Depending on your CUDA version the instructions vary. We show an example for the CUDA 11.8.

conda create --name misato python=3
conda activate misato
conda install -c anaconda pandas pip h5py
pip3 install torch --index-url https://download.pytorch.org/whl/cu118 --no-cache
pip install joblib matplotlib
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html
pip install pytorch-lightning==1.8.3
pip install torch-geometric
pip install ipykernel==5.5.5 ipywidgets==7.6.3 nglview==2.7.7
conda install -c conda-forge nb_conda_kernels

To run inference for MD you have to install ambertools. We recommend to install it in a separate conda environment.

conda create --name ambertools python=3
conda activate ambertools
conda install -c conda-forge ambertools nb_conda_kernels
pip install h5py jupyter ipykernel==5.5.5 ipywidgets==7.6.3 nglview==2.7.7

Citation

If you found this work useful please consider citing the article.