A scaled-up version of the comboKR (https://github.com/aalto-ics-kepaco/comboKR/) for drug combination surface prediction.
The code is developed with python 3.9. The main dependencies are the RLScore (https://github.com/aatapa/RLScore) and synergy (https://github.com/djwooten/synergy) packages. From these, the versions 0.8.2a (RLScore) and 0.5.1 (synergy) have been used.
Note:
- Synergy package is not backwards compatible! Newer versions than 0.5.1 currently exist, but using those will result in errors.
- The RLScore package (0.8.2a0) is not available in PyPI! When installing the environment with pip, it should be installed separately.
The main algorithm in scalable_comboKR.py has been run with numpy 1.23.5, and scikit-learn 1.0.2. The demo depends additionally on some other usual python packages, such as scipy and matplotlib.
A suitable conda environment can be created with the provided yml file, with which the algorithm can then be used.
conda env create -f environment.yml -n combokr2-env
Alternatively, the package can be installed with pip.
Before installing the comboKR2.0 package make sure that latest versions of pip and build are installed:
pip3 install --upgrade pip
pip3 install --upgrade build
There are two options for installing the comboKR package.
`pip3 install git+https://github.com/aalto-ics-kepaco/comboKR2.0.git#egg=comboKR2
mkdir comboKR2.0
cd comboKR2.0
git clone https://github.com/aalto-ics-kepaco/comboKR2.0
After downloading the comboKR2.0 package, it can be installed by the following command from the comboKR2.0 directory:
pip3 install .
The RLScore package (https://github.com/aatapa/RLScore) has to be installed separately.
A small-scale demo based on O'Neil dataset [1] is provided in demo.py. Before running it, download and unpack the data.zip. The expected runtime of the demo is about 20 minutes; much less if candidate set optimisation is used instead of the projected gradient descent.
[1] O'Neil, J., Benita, Y., Feldman, I., Chenard, M., Roberts, B., Liu, Y., ... & Shumway, S. D. (2016). An unbiased oncology compound screen to identify novel combination strategies. Molecular cancer therapeutics, 15(6), 1155-1162.