RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro

RECOVER is a platform that can guide wet lab experiments to quickly discover synergistic drug combinations active against a cancer cell line, requiring substantially less screening than an exhaustive evaluation (preprint).

Environment setup

Requirements: Anaconda (https://www.anaconda.com/) and Git LFS (https://github.com/proxy/git-lfs.github.com/). Please make sure both are installed on the system prior to running installation.

Installation: enter the command source install.sh and follow the instructions. This will create a conda environment named recover and install all the required package including the reservoir package that stores the primary data acquisition scripts.

Running the pipeline

Configuration files for our experiments are provided in the following directory: Recover/recover/config

To run the pipeline with a custom configuration:

• Create your configuration file and move it to Recover/recover/config/
• Run python train.py --config <my_configuration_file>

Note that <my_configuration_file> should not include .py. For example, to run the pipeline with configuration from the file model_evaluation.py, run python train.py --config model_evaluation

Note

This Recover repository is based on research funded by (or in part by) the Bill & Melinda Gates Foundation. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation.