  Medicinal synergy prediction is a powerful tool in drug discovery and
development that harnesses the principles of combination therapy to enhance
therapeutic outcomes by improving efficacy, reducing toxicity, and preventing
drug resistance. While a myriad of computational methods has emerged for
predicting synergistic drug combinations, a large portion of them may overlook
the intricate, yet critical relationships between various entities in drug
interaction networks, such as drugs, cell lines, and diseases. These
relationships are complex and multidimensional, requiring sophisticated
modeling to capture nuanced interplay that can significantly influence
therapeutic efficacy. We introduce a salient deep hypergraph learning method,
namely, Heterogeneous Entity Representation for MEdicinal Synergy prediction
(HERMES), to predict anti-cancer drug synergy. HERMES integrates heterogeneous
data sources, encompassing drug, cell line, and disease information, to provide
a comprehensive understanding of the interactions involved. By leveraging
advanced hypergraph neural networks with gated residual mechanisms, HERMES can
effectively learn complex relationships/interactions within the data. Our
results show HERMES demonstrates state-of-the-art performance, particularly in
forecasting new drug combinations, significantly surpassing previous methods.
This advancement underscores the potential of HERMES to facilitate more
effective and precise drug combination predictions, thereby enhancing the
development of novel therapeutic strategies.
