DEDTI versus IEDTI: efficient and predictive models of drug-target interactions.

ABSTRACT: Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representati

SUBMITTER: Zabihian A 

PROVIDER: S-EPMC10247802 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature