# PepGB: Facilitating peptide drug discovery via graph neural networks

arXiv: https://arxiv.org/abs/2401.14665 · HTML: https://mlsanity.com/paper/2401.14665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification](https://mlsanity.com/paper/2012.03173/) (2020)
- [Learning Hierarchical Protein Representations via Complete 3D Graph Networks](https://mlsanity.com/paper/2207.12600/) (2022)
- [DropMessage: Unifying Random Dropping for Graph Neural Networks](https://mlsanity.com/paper/2204.10037/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation](https://mlsanity.com/paper/2402.08703/) (2024)

## Abstract (arXiv)

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.