PiscesCSM: prediction of anticancer synergistic drug combinations.

ABSTRACT: While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinatio

SUBMITTER: AlJarf R 

PROVIDER: S-EPMC11264925 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature