Heterogeneous entity representation for medicinal synergy prediction.

ABSTRACT:

Motivation

Forecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy.

Results

We present a novel deep hypergraph learning method named Heterogeneous Entity Representation for MEdicinal Synergy (HERMES) prediction to predict the synergistic effects of anti-cancer dr

SUBMITTER: Wu J 

PROVIDER: S-EPMC11745903 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature