Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction.

ABSTRACT:

Motivation

Characterizing drug-protein interactions (DPIs) is crucial to the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict DPIs without human trial and error. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here, we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset.

Results

At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estima

SUBMITTER: Kim Q 

PROVIDER: S-EPMC8545317 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature