ICAN: Interpretable cross-attention network for identifying drug and target protein interactions.

ABSTRACT: Drug-target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. How

SUBMITTER: Kurata H 

PROVIDER: S-EPMC9591068 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature