MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders.

ABSTRACT: Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and sh

SUBMITTER: Zhang P 

PROVIDER: S-EPMC10027223 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature