MacroSimGNN: Efficient and Accurate Prediction of Macromolecule Pairwise Similarity via A Graph Neural Network

Published

February 16, 2026

Author(s)

Jiale Shi, Dylan Walsh, Runzhong Wang, Nathan Rebello, Bradley Olsen, Debra Audus

Abstract

Efficient and accurate prediction of macromolecule pairwise similarity is essential for developing database search engines and is useful for machine learning based predictive tools. Existing methods for calculating macromolecular similarity suffer from significant drawbacks. Graph edit distance is accurate but computationally expensive, and graph kernel methods are computationally efficient but inaccurate. This study introduces a graph neural network model, MacroSimGNN, which significantly improves computational efficiency while maintaining high accuracy on macromolecule pairwise similarity. Furthermore, this approach enables feature embeddings based on macromolecular similarities to a set of landmark molecules, enhancing both unsupervised and supervised learning tasks. This method represents a significant advancement in macromolecular cheminformatics, paving the way for the development of advanced search engines and data-driven design of macromolecules.

Citation

Macromolecules

Pub Type

Journals

Download Paper

https://doi.org/10.1021/acs.macromol.5c03015

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Keywords

polymers, similarity, graph neural networks

Citation

Shi, J. , Walsh, D. , Wang, R. , Rebello, N. , Olsen, B. and Audus, D. (2026), MacroSimGNN: Efficient and Accurate Prediction of Macromolecule Pairwise Similarity via A Graph Neural Network, Macromolecules, [online], https://doi.org/10.1021/acs.macromol.5c03015, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958779 (Accessed May 1, 2026)

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