Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with highthroughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to ∼ 20% correlation and ∼ 40% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs.
Halil Ibrahim Kuru, Oznur Tastan, A. Ercument Cicek Our paper is available at bioRxiv
- Python 3.7
- Numpy 1.18.1
- Scipy 1.4.1
- Pandas 1.0.1
- Tensorflow 2.1.0
- Tensorflow-gpu 2.1.0
- Scikit-Learn 0.22.1
- keras-metrics 1.1.0
- h5py 2.10.0
- cudnn 7.6.5 (for gpu support only)
Raw data of drug combinations are taken from DrugComb
Drug chemical features are calculated by Chemopy
RMA normalized E-MTAB-3610 untrated cell line gene expression data is downloaded from cancerrxgene
You can download preprocessed data from link, extract all files into data/
$ python main.py --saved-model-name matchmaker.h5 --train-test-mode 1Download pretrained weights from link
$ python main.py --saved-model-name matchmaker_saved.h5 --train-test-mode 0We build a command line application MatchMakerApp. You can play and predict synergy of any two drugs by using their PubChem CIDs.
- Zagidullin, B., Aldahdooh, J., Zheng, S., Wang, W., Wang, Y., Saad, J., ... & Tang, J. (2019). DrugComb: an integrative cancer drug combination data portal. Nucleic acids research, 47(W1), W43-W51.
- CCao, D. S., Xu, Q. S., Hu, Q. N., & Liang, Y. Z. (2013). ChemoPy: freely available python package for computational biology and chemoinformatics. Bioinformatics, 29(8), 1092-1094.
- [dataset] Francesco Iorio (2015). Transcriptional Profiling of 1,000 human cancer cell lines, arrayexpress-repository, V1. https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3610.