# Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

arXiv: https://arxiv.org/abs/2105.06709 · HTML: https://mlsanity.com/paper/2105.06709/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)

## Built on by (most cited first, 24 of 24)

- [OntoProtein: Protein Pretraining With Gene Ontology Embedding](https://mlsanity.com/paper/2201.11147/) (2022)
- [BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs](https://mlsanity.com/paper/2310.03320/) (2023)
- [MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding](https://mlsanity.com/paper/2402.14391/) (2024)
- [SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2305.08316/) (2023)
- [Fair Graph Representation Learning via Sensitive Attribute Disentanglement](https://mlsanity.com/paper/2405.07011/) (2024)
- [ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2405.06649/) (2024)
- [ProtCLIP: Function-Informed Protein Multi-Modal Learning](https://mlsanity.com/paper/2412.20014/) (2024)
- [Multi-level Interaction Modeling for Protein Mutational Effect Prediction](https://mlsanity.com/paper/2405.17802/) (2024)
- [GLProtein: Global-and-Local Structure Aware Protein Representation Learning](https://mlsanity.com/paper/2506.06294/) (2025)
- [Towards Unified AI Drug Discovery with Multiple Knowledge Modalities](https://mlsanity.com/paper/2305.01523/) (2023)
- [Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling](https://mlsanity.com/paper/2301.13154/) (2023)
- [Variational Disentangled Graph Auto-Encoders for Link Prediction](https://mlsanity.com/paper/2306.11315/) (2023)
- [Graph Neural Networks for Protein-Protein Interactions -- A Short Survey](https://mlsanity.com/paper/2404.10450/) (2024)
- [GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning](https://mlsanity.com/paper/2408.00057/) (2024)
- [Prompt-Guided Injection of Conformation to Pre-trained Protein Model](https://mlsanity.com/paper/2202.02944/) (2022)
- [Contrastive Disentangled Learning on Graph for Node Classification](https://mlsanity.com/paper/2306.11344/) (2023)
- [Deep Manifold Transformation for Protein Representation Learning](https://mlsanity.com/paper/2402.09416/) (2024)
- [PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs](https://mlsanity.com/paper/2507.05101/) (2025)
- [Graph Anisotropic Diffusion](https://mlsanity.com/paper/2205.00354/) (2022)
- [Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms](https://mlsanity.com/paper/2507.02724/) (2025)
- [A Multimodal Human Protein Embeddings Database: DeepDrug Protein Embeddings Bank (DPEB)](https://mlsanity.com/paper/2510.22008/) (2025)
- [Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach](https://mlsanity.com/paper/2605.09964/) (2026)
- [Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery](https://mlsanity.com/paper/2605.21522/) (2026)
- [Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding](https://mlsanity.com/paper/2606.02629/) (2026)

## Abstract (arXiv)

The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms. Existing methods suffer from significant performance degradation when tested in unseen dataset. In this paper, we investigate the problem and find that it is mainly attributed to the poor performance for inter-novel-protein interaction prediction. However, current evaluations overlook the inter-novel-protein interactions, and thus fail to give an instructive assessment. As a result, we propose to address the problem from both the evaluation and the methodology. Firstly, we design a new evaluation framework that fully respects the inter-novel-protein interactions and gives consistent assessment across datasets. Secondly, we argue that correlations between proteins must provide useful information for analysis of novel proteins, and based on this, we propose a graph neural network based method (GNN-PPI) for better inter-novel-protein interaction prediction. Experimental results on real-world datasets of different scales demonstrate that GNN-PPI significantly outperforms state-of-the-art PPI prediction methods, especially for the inter-novel-protein interaction prediction.