Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction.

ABSTRACT:

Background

Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in drug development. Several computational methods have been developed to allow rapid and efficient large-scale screening of peptide toxicity. However, these methods mainly rely on the primary sequence and often ignore critical structural information, which limits their predictive accuracy.

Results

In this study, we introduce a novel framework named StrucToxNet that integrates a pre-trained protein language model with an equivariant graph neural network to improve peptide toxicity prediction. By combining sequence embeddings from the

SUBMITTER: Jiao S 

PROVIDER: S-EPMC12306123 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature