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!
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. 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=comboKR2.0
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 .
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.