This repo provides examples of performing virtual screening related work. The idea is to provide well defined and well documented examples that can be run by third parties, and to define how to produce a toolset that allows these workflows to be readily executed.
A key related project is Pipelines that defines a number of components (currently mostly based on Python and RDKit).
A key part of the strategy is perform the execution in Docker containers so that you do NOT need to install lots of different tools on your host machine. Currently the only tools you need installed are:
Many of the Docker images can be found on the Informatics Matters Docker Hub repository.
This is an upstream project for the Squonk computational notebook, as is Pipelines. The aim is that these workflows are generated in a way that makes them easy to integrated into Squonk. As such it provides a playground where new methodologies can be developed and benchmarked.
Inlcuded in this repo are a number of public datasets that are useful for testing and validation studies. You can find them in the datasets directory. Feel free to contribute additional datasets, but if doing so please include documentation describing the source of the dataset and attribute ownership appropriately.
- CDK2 virtual screening with rDock
- CDK validation using DEKIOS data
- Generating ROC curves
- Docking pose validation for ESR
- rDock setup for use in Squonk
We welcome contributions, but want to make sure they follow a well defined set of pattens and conventions. Unfortunately these are still being established.
We will insist on all examples being well documented.
Contact Tim Dudgeon <tdudgeon at informaticsmatters dot com> if you want to get involved.
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