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Physics-based molecular fingerprints from spectral graph theory provide efficient geometry-aware measures of chemical similarity

Year: 2026

Type: article

Abstract: (3) invariance. Spectral fingerprints differentiate between unique molecular structures with identical 2D connectivity, overcoming a limitation of 2D descriptors, while maintaining the low computational cost needed for efficient screening of vast chemical spaces. We evaluate our fingerprints with community detection algorithms and observe strong performance against representative baselines across datasets from organic, inorganic, biological, reticular, and reaction chemistry. Nearest-neighbor property estimation and applicability domain analyses reveal the utility of our molecular representation in machine learning and cheminformatics. We anticipate that spectral fingerprints will serve as interpretable and efficient measures of chemical similarity that incorporate 3D information at minimal cost and generalize well across diverse chemical domains.

Source: Chemical Science

DOI: 10.1039/d6sc06309h

Authors: Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich, Heather J. Kulik

Institutions: Massachusetts Institute of Technology, University of Stuttgart

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Alfred P. Sloan Foundation, Dow Chemical Company, Massachusetts Institute of Technology

OpenAlex ID: W7201852280

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