PepGB: Facilitating peptide drug discovery via graph neural networks

TL;DR

PepGB is a novel graph neural network framework that improves peptide-protein interaction prediction, aiding early peptide drug discovery with enhanced accuracy and handling of imbalanced data.

Contribution

The paper introduces PepGB and diPepGB, innovative deep learning models that advance peptide drug discovery by addressing data imbalance and improving prediction accuracy.

Findings

PepGB outperforms baseline models in predicting peptide-protein interactions.

diPepGB effectively models imbalanced data in peptide lead optimization.

The frameworks facilitate identification of novel peptide drugs and targets.

Abstract

Peptides offer great biomedical potential and serve as promising drug candidates. Currently, the majority of approved peptide drugs are directly derived from well-explored natural human peptides. It is quite necessary to utilize advanced deep learning techniques to identify novel peptide drugs in the vast, unexplored biochemical space. Despite various in silico methods having been developed to accelerate peptide early drug discovery, existing models face challenges of overfitting and lacking generalizability due to the limited size, imbalanced distribution and inconsistent quality of experimental data. In this study, we propose PepGB, a deep learning framework to facilitate peptide early drug discovery by predicting peptide-protein interactions (PepPIs). Employing graph neural networks, PepGB incorporates a fine-grained perturbation module and a dual-view objective with contrastive learning-based peptide pre-trained representation to predict PepPIs. Through rigorous evaluations, we demonstrated that PepGB greatly outperforms baselines and can accurately identify PepPIs for novel targets and peptide hits, thereby contributing to the target identification and hit discovery processes. Next, we derive an extended version, diPepGB, to tackle the bottleneck of modeling highly imbalanced data prevalent in lead generation and optimization processes. Utilizing directed edges to represent relative binding strength between two peptide nodes, diPepGB achieves superior performance in real-world assays. In summary, our proposed frameworks can serve as potent tools to facilitate peptide early drug discovery.