asapdiscovery

A toolkit for structure-based open antiviral drug discovery by the ASAP Discovery Consortium.

Intro

All pandemics are global health threats. Our best defense is a healthy global antiviral discovery community with a robust pipeline of open discovery tools. The AI-driven Structure-enabled Antiviral Platform (ASAP) is making this a reality!

The toolkit in this repo is a batteries-included drug discovery pipeline being actively developed in a transparent open-source way, with a focus on computational chemistry and informatics support for medicinal chemistry. Coupled with ASAP's active data disclosures our campaign to develop a new series of antivirals can provide insight into the drug discovery process that is normally conducted behind closed doors.

Getting Started

Install the asapdiscovery subpackages and begin to explore! Our docs can be found here.

There are a range of workflows and tooling to use split into several namespace subpackages by theme.

asapdiscovery-alchemy: Free energy calculations using OpenFE and Alchemiscale

asapdiscovery-cli: Command line tools uniting the whole repo.

asapdiscovery-data: Core data models and integrations with services such as Postera.ai

asapdiscovery-dataviz: Data and structure visualization using 3DMol and PyMOL

asapdiscovery-docking: Docking and compound screening with the OpenEye toolkit

asapdiscovery-genetics: Working with sequence and fitness information

asapdiscovery-ml: Structure based ML models for predicting compound activity

asapdiscovery-modelling: Structure prep and standardisation

asapdiscovery-simulation: MD simulations and analysis using OpenMM

asapdiscovery-workflows: Workflows that combine components to enable project support

Disclaimer

asapdiscovery is pre-alpha and is under very active development, we make no guarantees around correctness and the API is liable to change rapidly at any time.

Installation

Note: currently all asapdiscovery packages support Python 3.10 only.

asapdiscovery is a namespace package, composed of individual Python packages with their own dependencies. Each of these packages follows the asapdiscovery-* convention for the package name, e.g. asapdiscovery-data.

To install an asapdiscovery package hosted in this repository, we recommend the following:

1. Clone the repository, then enter the source tree:

   git clone https://github.com/choderalab/asapdiscovery.git
   cd asapdiscovery
   

2. Install the dependencies into a new conda environment, and activate it: NOTES: Conda will almost certainly fail to build the environment - mamba is a drop-in replacement for conda that is much faster and more reliable. Additionally, if the environment is built on a CPU, torch may not compile with GPU support. Instead, build the environment as described on a GPU node; the architecture will be detected automatically. Alternatively to build for a specific CUDA version you can use the

   mamba env create -f devtools/conda-envs/asapdiscovery-{platform}.yml
   conda activate asapdiscovery
   

   Alternatively to build for a specific CUDA version you can use the following.

   export CONDA_OVERRIDE_CUDA=12.2 && mamba env create -f devtools/conda-envs/asapdiscovery-{platform}.yml && conda activate asapdiscovery
   

3. Install the individual asapdiscovery packages you want to use with pip into the environment. For example, asapdiscovery-data:

   pip install asapdiscovery-data
   

Contributing

pre-commit

We use pre-commit to automate code formatting and other fixes. You do not need to install pre-commit as we run it on our CI. If you want to run it locally:

# install
$ mamba install -c conda-forge pre-commit
# check
$ pre-commit --version
pre-commit 3.0.4 # your version may be different
$ pre-commit install

Now every time you make a commit, the hooks will run on just the files you changed. See here for more details.

Copyright

Copyright (c) 2023, ASAP Discovery

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.6.