Representing chemical and enzymatic reactions in fingerprint space for applicability filtering and classification

Abstract Molecular and reaction fingerprints are widely used as static descriptors, but their relationship to graph-level reaction application remains unclear. We address this question in counted Extended-Connectivity Fingerprint (ECFP) space, representing reactions as signed differences between product and substrate fingerprints. We define ECFP-compatible templates, for which graph-level reaction application induces a constant fingerprint displacement, yielding a functorial mapping from graph transformations to affine translations in fingerprint space. We also introduce reaction-center ECFPs to encode the local environments required for template application and to provide a fast coordinate-wise prefilter for graph-level reaction applicability. We evaluate this framework on USPTO-50k and MetaNetX reactions across a range of ECFP radii. Both representations carry predictive information for reaction-class and enzyme-class classification, with reaction-center ECFPs contributing information not captured by reaction ECFPs alone. This framework connects reaction templates and reaction fingerprints to molecular pathway search and reaction-property prediction in fingerprint space.

Authors

Institutions

Publication Details

Journal
Journal of Cheminformatics
Published
2026-11-06
DOI
https://doi.org/10.1186/s13321-026-01322-2
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.79

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Representing chemical and enzymatic reactions in fingerprint space for applicability filtering and classification

Guillaume Gricourt, Jean‐Loup Faulon, Philippe Meyer, Thomas Duigou
1 citations
Journal of Cheminformatics
Computational Drug Discovery Methods
1.79
article

Representing chemical and enzymatic reactions in fingerprint space for applicability filtering and classification

Guillaume Gricourt, Jean‐Loup Faulon, Philippe Meyer, Thomas Duigou
article en
1 citations

Abstract

Abstract Molecular and reaction fingerprints are widely used as static descriptors, but their relationship to graph-level reaction application remains unclear. We address this question in counted Extended-Connectivity Fingerprint (ECFP) space, representing reactions as signed differences between product and substrate fingerprints. We define ECFP-compatible templates, for which graph-level reaction application induces a constant fingerprint displacement, yielding a functorial mapping from graph transformations to affine translations in fingerprint space. We also introduce reaction-center ECFPs to encode the local environments required for template application and to provide a fast coordinate-wise prefilter for graph-level reaction applicability. We evaluate this framework on USPTO-50k and MetaNetX reactions across a range of ECFP radii. Both representations carry predictive information for reaction-class and enzyme-class classification, with reaction-center ECFPs contributing information not captured by reaction ECFPs alone. This framework connects reaction templates and reaction fingerprints to molecular pathway search and reaction-property prediction in fingerprint space.

Journal of Cheminformatics
AgroParisTech (FR), Université Paris-Saclay (FR), Microbiologie de l’alimentation au service de la santé (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 10%
Computational Drug Discovery Methods
1.79
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon