HIG-Syn: a hypergraph and interaction-aware multigranularity network for predicting synergistic drug combinations

TL;DR

This paper introduces HIG-Syn, a new deep learning model that improves predictions of drug combinations that work well together.

Contribution

HIG-Syn integrates hypergraph and interaction-aware modules to better capture biological interactions for drug synergy prediction.

Findings

HIG-Syn outperforms existing machine learning models in predicting drug synergy.

Five of the predicted drug combinations are supported by experimental evidence in the literature.

Abstract

Drug combinations can not only enhance drug efficacy but also effectively reduce toxic side effects and mitigate drug resistance. With the advancement of drug combination screening technologies, large amounts of data have been generated. The availability of large data enables researchers to develop deep learning methods for predicting drug targets for synergistic combination. However, these methods still lack sufficient accuracy for practical use, and most overlook the biological significance of their models.

We propose the HIG-Syn (hypergraph and interaction-aware multigranularity network for drug synergy prediction) model, which integrates a coarse-granularity module and a fine-granularity module to predict drug combination synergy. The former utilizes a hypergraph to capture global features, while the latter employs interaction-aware attention to simulate biological processes by modeling substructure–substructure and substructure–cell line interactions. HIG-Syn outperforms state-of-the-art machine learning models on our validation datasets extracted from the DrugComb and GDSC2 databases. Furthermore, the fact that five of the 12 novel synergistic drug combinations predicted by HIG-Syn are strongly supported by experimental evidence in the literature underscores its practical potential.

The source code is available at https://github.com/gracygyx/HIGSyn

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