ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.

ABSTRACT:

Motivation

Assessing the potential toxicity of proteins is crucial for both therapeutic and agricultural applications. Traditional experimental methods for protein toxicity evaluation are time-consuming, expensive, and labor-intensive, highlighting the requirement for efficient computational approaches. Recent advancements in language models and deep learning have significantly improved protein toxicity prediction, yet current models often lack the ability to integrate evolutionary and structural information, which is crucial for accurate toxicity assessment of proteins.

Results

In this study, we present ToxDL 2.0, a novel multimodal deep learning model for protein toxicity prediction that integrates both evolutionary and structural information derived from a pretrained lang

SUBMITTER: Zhu L 

PROVIDER: S-EPMC12018212 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature