Package: 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 refinedMolecular Dynamics: 16972 simulated protein-ligand structures, 10 ns eachAI: pytorch dataloaders, 3 base line models for MD and QM andbinding affinity prediction:electron: Vision:We are a drug discovery community project :hugs:highest possible accuracy for ligand moleculesrepresent the systems dynamics in reasonable timescalesinnovative AI models for drug discovery predictionsLets crack the100+ nsMD,30000+ protein-ligand structuresand a whole new world ofAI models for drug discoverytogether.Check out the paper!:purple_heart: CommunityWant to get hands-on for drug discovery using AI?Join our discord server!Check out our Hugging Face spaces to run and visualize theadaptability modeland to performQM property predictions.📌  IntroductionIn 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 theFULL MISATO datasetfromZenodousing the links below:MD (133 GiB)QM (0.3 GiB)electronic densities (6 GiB)MD restart and topology files (55 GiB)wget-Odata/MD/h5_files/MD.hdf5https://zenodo.org/record/7711953/files/MD.hdf5
wget-Odata/QM/h5_files/QM.hdf5https://zenodo.org/record/7711953/files/QM.hdf5Start with the notebooksrc/getting_started.ipynbto :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.🚀  QuickstartWe recommend to pull our MISATO image from DockerHub or to create your own image (seedocker/).  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 projectgitclonehttps://github.com/t7morgen/misato-dataset.gitcdmisato-datasetFor singularity use:# get the container imagesingularitypulldocker://sab148/misato-dataset
singularityshellmisato.sifFor docker use:sudodockerpullsab148/misato-dataset:latest
bashdocker/run_bash_in_container.shProject 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.mdInstallation using your own conda environmentIn 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/installDepending on your CUDA version the instructions vary. We show an example for the CUDA 11.8.condacreate--namemisatopython=3condaactivatemisato
condainstall-canacondapandaspiph5py
pip3installtorch--index-urlhttps://download.pytorch.org/whl/cu118--no-cache
pipinstalljoblibmatplotlib
pipinstallpyg_libtorch_scattertorch_sparsetorch_clustertorch_spline_conv-fhttps://data.pyg.org/whl/torch-2.0.0+cu118.html
pipinstallpytorch-lightning==1.8.3
pipinstalltorch-geometric
pipinstallipykernel==5.5.5ipywidgets==7.6.3nglview==2.7.7
condainstall-cconda-forgenb_conda_kernelsTo run inference for MD you have to install ambertools. We recommend to install it in a separate conda environment.condacreate--nameambertoolspython=3condaactivateambertools
condainstall-cconda-forgeambertoolsnb_conda_kernels
pipinstallh5pyjupyteripykernel==5.5.5ipywidgets==7.6.3nglview==2.7.7CitationIf you found this work useful please consider citing the article.