Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

ABSTRACT:

Background

Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results.

Results

In this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound-protein interaction site and binding affinity prediction. We used several kinds of self-supervised protein embeddings to enrich our protein inputs and used convolutional neural networks to extract features from them. Our results demonstrat

SUBMITTER: Wu J 

PROVIDER: S-EPMC9756525 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature