Work

GraphPepTox: a structure-aware pre-trained framework for peptide toxicity prediction using geometric graph transformer and LoRA-based fine-tuning

Year: 2026

Type: article

Abstract: ABSTRACT Peptides are widely used as potential therapeutic agents in drug discovery and biotechnology because they are specific, effective, and relatively inexpensive to produce. They are used in drug development, vaccines, and antimicrobial treatments. However, peptide toxicity remains a major concern as it offers unwanted toxic consequences, such as membrane rupture, haemolysis, tissue damage and adverse immunological response. Early detection of toxic peptide candidates is vital for the development of safe and effective therapies. Current computational methods for predicting peptide toxicity are largely based on hand-crafted sequence descriptors or sequence-only deep learning architectures that may not fully account for the underlying 3-dimensional structural determinants of peptide toxicity. We introduce GraphTox, a structure-aware geometric deep learning framework which combines self-supervised graph representation learning with hierarchical structural modelling to accurately predict peptide toxicity. Our framework learns geometry-aware embeddings from peptide structural graphs via self-supervised masked residue reconstruction, based on a Masked Graph Autoencoder (MGAE) built on a Geometric Graph Transformer (GGT) encoder. The pretrained structural representations are cross fused via a multi-scale U-Net architecture to capture both local residue-level interactions and global conformational patterns associated with peptide toxicity. GraphTox explicitly models spatial relationships between residues, thereby efficiently capturing structural aspects that are generally neglected by sequence-based predictors, such as residue clustering, hydrophobic interactions and electrostatic organization. On benchmark datasets our framework shows superior performance and interpretability over the existing state-of-the-art methods. Our hybrid hierarchical structural modelling framework is a superior computational platform to improve the prediction of peptide toxicity and expedite the creation of safer peptide therapies. https://github.com/debraj-55555/GraphTox

Source: BMC Bioinformatics

DOI: 10.1186/s12859-026-06744-1

Authors: Soumyadeep Bhaduri, Debraj Das, Pralay Mitra

Institution: Indian Institute of Technology Kharagpur

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Drug Discovery

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990