DDoS: A Graph neural Network Based Drug Synergy Prediction Algorithm

Kyriakos Schwarz, Pliego Mendieta Alicia, Amina Mollaysa, Planas-Paz Lara, Chantal Pauli, Ahmed Allam, Michael Krauthammer
Proceedings of the fifth Conference on Health, Inference, and Learning, PMLR 248:24-38, 2024.

Abstract

Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of potential drug combinations, prompts a growing interest in computational approaches for predicting synergies in untested drug pairs. We introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We extract data from the largest available drug combination database (DrugComb) and generate multiple synergy scores (commonly used in the literature) to create seven datasets that serve as a reliable benchmark with high confidence. In contrast to conventional models relying on pre-computed chemical features, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs, providing superior performance in predicting drug synergies. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v248-schwarz24a, title = {DDoS: A Graph neural Network Based Drug Synergy Prediction Algorithm}, author = {Schwarz, Kyriakos and Mendieta Alicia, Pliego and Mollaysa, Amina and Lara, Planas-Paz and Pauli, Chantal and Allam, Ahmed and Krauthammer, Michael}, booktitle = {Proceedings of the fifth Conference on Health, Inference, and Learning}, pages = {24--38}, year = {2024}, editor = {Pollard, Tom and Choi, Edward and Singhal, Pankhuri and Hughes, Michael and Sizikova, Elena and Mortazavi, Bobak and Chen, Irene and Wang, Fei and Sarker, Tasmie and McDermott, Matthew and Ghassemi, Marzyeh}, volume = {248}, series = {Proceedings of Machine Learning Research}, month = {27--28 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v248/main/assets/schwarz24a/schwarz24a.pdf}, url = {https://proceedings.mlr.press/v248/schwarz24a.html}, abstract = {Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of potential drug combinations, prompts a growing interest in computational approaches for predicting synergies in untested drug pairs. We introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We extract data from the largest available drug combination database (DrugComb) and generate multiple synergy scores (commonly used in the literature) to create seven datasets that serve as a reliable benchmark with high confidence. In contrast to conventional models relying on pre-computed chemical features, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs, providing superior performance in predicting drug synergies. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.} }
Endnote
%0 Conference Paper %T DDoS: A Graph neural Network Based Drug Synergy Prediction Algorithm %A Kyriakos Schwarz %A Pliego Mendieta Alicia %A Amina Mollaysa %A Planas-Paz Lara %A Chantal Pauli %A Ahmed Allam %A Michael Krauthammer %B Proceedings of the fifth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2024 %E Tom Pollard %E Edward Choi %E Pankhuri Singhal %E Michael Hughes %E Elena Sizikova %E Bobak Mortazavi %E Irene Chen %E Fei Wang %E Tasmie Sarker %E Matthew McDermott %E Marzyeh Ghassemi %F pmlr-v248-schwarz24a %I PMLR %P 24--38 %U https://proceedings.mlr.press/v248/schwarz24a.html %V 248 %X Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of potential drug combinations, prompts a growing interest in computational approaches for predicting synergies in untested drug pairs. We introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We extract data from the largest available drug combination database (DrugComb) and generate multiple synergy scores (commonly used in the literature) to create seven datasets that serve as a reliable benchmark with high confidence. In contrast to conventional models relying on pre-computed chemical features, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs, providing superior performance in predicting drug synergies. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.
APA
Schwarz, K., Mendieta Alicia, P., Mollaysa, A., Lara, P., Pauli, C., Allam, A. & Krauthammer, M.. (2024). DDoS: A Graph neural Network Based Drug Synergy Prediction Algorithm. Proceedings of the fifth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 248:24-38 Available from https://proceedings.mlr.press/v248/schwarz24a.html.

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