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.

利用深度学习技术，本文提出了 PepGB 和 diPepGB 两个深度学习框架，用于预测肽蛋白相互作用关系，以促进肽类早期药物发现，提高目标识别和化合物发现过程中的效率。