GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

ABSTRACT:

Objective

To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

Materials and methods

We propose a deep learning framework named Graph Convolutional Network for Drug Synergy (GraphSynergy). GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order topological relationships in the PPI network of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxicity scores. An attention component is also introduced in GraphSynergy, which aims to capture the pivotal proteins that

SUBMITTER: Yang J 

PROVIDER: S-EPMC8510276 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature