DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

• URL: http://arxiv.org/abs/2210.00802v3
• Date: Fri, 26 Apr 2024 07:23:20 GMT
• Title: DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
• Authors: Kyriakos Schwarz, Alicia Pliego-Mendieta, Amina Mollaysa, Lara Planas-Paz, Chantal Pauli, Ahmed Allam, Michael Krauthammer,
• Abstract summary: We introduce a Graph Neural Network (textitGNN) based model for drug synergy prediction. In contrast to conventional models, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs. 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.
• Score: 0.521420263116111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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