SkipGNN: predicting molecular interactions with skip-graph networks
Open accessAbstract
Molecular interaction networks are powerful resources for molecular discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. While deep learning on graphs has dramatically advanced the prediction prowess, current graph neural network (GNN) methods are mainly optimized for prediction on the basis of direct similarity between interacting nodes. In biological networks, however, similarity between nodes that do not directly interact has proved incredibly useful in the last decade across a variety of interaction networks. Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions. SkipGNN predicts molecular interactions by not only aggregating information from direct interactions but also from second-order interactions, which we call skip similarity. In contrast to existing GNNs, SkipGNN receives neural messages from two-hop neighbors as well as immediate neighbors in the interaction network and non-linearly transforms the messages to obtain useful information for prediction. To inject skip similarity into a GNN, we construct a modified version of the original network, called the skip graph. We then develop an iterative fusion scheme that optimizes a GNN using both the skip graph and the original graph. Experiments on four interaction networks, including drug–drug, drug–target, protein–protein, and gene–disease interactions, show that SkipGNN achieves superior and robust performance. Furthermore, we show that unlike popular GNNs, SkipGNN learns biologically meaningful embeddings and performs especially well on noisy, incomplete interaction networks.
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The inductive ability of graph convolution neural networks to generalize to unseen data and to generate new data by decoding latent representations has enabled discovery of new molecules, interactions, and functions.
“Specifically, the inductive ability of graph convolution neural networks to generalize to data points unseen during model training (Section 2.2) and even generate new data points from scratch by decoding latent representation from the embedding space (Section 2.3) has enabled the discovery of new molecules, interactions, and functions [101, 108, 109].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [101]
Unlike machine learning approaches for medical images and biological sequences, graph representation learning methods are uniquely able to use structural information in multimodal datasets.
“Unlike machine learning approaches designed to analyze data modalities like medical images and biological sequences, graph representation learning methods are uniquely able to leverage structural information in multimodal datasets [101].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [101]
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- Authors
- Huang, K., Xiao, C., Glass, L. M., Zitnik, M., & Sun, J.
- Affiliations
- Harvard University (US)
- IQVIA (United States) (US)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Sun, Jimeng
- Venue
- Scientific Reports
- Published
- 2020-12-03
- Volume
- 10
- Issue
- 1
- Pages
- 21092-21092
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7713130
- OpenAlex
- W3108458441
- Cited by
- 133
- References
- 65
- Citations by year
- 2026: 14 · 2025: 28 · 2024: 34 · 2023: 18 · 2022: 24 · 2021: 12 · 2020: 3
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- arXiv (Cornell University) (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- graph neural networks, molecular interaction prediction, second-order interactions, skip graphs, drug-drug interactions, drug-target interactions, protein-protein interaction, gene-disease interaction, node embeddings
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