Topology-enhanced molecular graph representation for anti-breast cancer drug selection.

ABSTRACT:

Background

Breast cancer is currently one of the cancers with a higher mortality rate in the world. The biological research on anti-breast cancer drugs focuses on the activity of estrogen receptors alpha (ER[Formula: see text]), the pharmacokinetic properties and the safety of the compounds, which, however, is an expensive and time-consuming process. Developments of deep learning bring potential to efficiently facilitate the candidate drug selection against breast cancer.

Methods

In this paper, we propose an Anti-Breast Cancer Drug selection method utilizing Gated Graph Neural Networks (ABCD-GGNN) to topologically enhance the molecular representation of candidate drugs. By constructing atom-level graphs through atomic descriptors for each distinct compound, ABCD-GGNN can top

SUBMITTER: Gao Y 

PROVIDER: S-EPMC9484163 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature