Machine Learning Solvers for the Directory of Useful Decoys - Enhanced (DUD-E)

Motivation

This project assess the performance of various machine learning algorithms for the Directory of Useful Decoys - Enhanced (DUD-E).

The performance of the various algorithms is assessed by means of calculating the training scores, the log-loss, the confusion matrix, the f1-score, by plotting the ROC and computing the AUROC.

This code was created as an assignment for the Data Science module, at the EPSRC CDT in Sustainable Approached to Biomedical Sciences: Responsible and Reproducible Research - SABS R3

Installation

In order to install the packaged, the user will require the presence of Python3 and the pip3 installer.

For installation on Linux or OSX, use the following commands. This will create an environment and automatically install all the requirements.

python3 -m venv env
source env/bin/activate
pip install -e .

Usage

In order to run the solver, type the following commands int the activated python environment.

jupyter notebook

Following this, the user should open the MAIN.ipynb and run the commands.

For running commands for specific DUD-E Targets, run the MAIN->target name>.ipynb files.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

BSD 3-Clause License © Jakke Neiro, Andrei Roibu

Credits

The team would like to express their gratitude to Tom Hadfield for his help and support during the development of this code.