A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.

ABSTRACT: Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature

SUBMITTER: Huang Y 

PROVIDER: S-EPMC10531393 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature