SMARTS-RX: a SMARTS-based representation of chemical functions for reactivity analysis

Chemical functional group annotation provides a mechanistically meaningful framework to interpret model outcomes and guide synthetic strategies. Here, we present SMARTS-RX-a curated, hierarchical ontology of 406 SMARTS-based functional group descriptors-designed to characterize chemically relevant and reactive functionalities in small molecules. SMARTS-RX achieves a balance between granularity and computational efficiency by focusing on functional groups central to pharmaceutical synthesis and medicinal chemistry. We describe the development of SMARTS-RX, including its systematic nomenclature and SMARTS encoding, which enable precise tracking of chemical environments. The utility of SMARTS-RX for mapping chemical reactivity is demonstrated through analyses of functional group distributions across major reaction types, using large-scale datasets from AstraZeneca's Electronic Lab Notebooks and Reaxys. Finally, we illustrate how this SMARTS library can be applied to guide building-block selection from commercial catalogues. A public GitHub repository has been created aiming for a continuous improvement of the current SMARTS_RX.Scientific Contribution: SMARTS-RX introduces a curated, hierarchical ontology of 406 SMARTS-based descriptors prioritizing pharmaceutical relevance and mechanistic interpretability. Distinct from prior efforts, SMARTS-RX encodes detailed chemical environments to improve reactivity mapping and feature extraction for both expert analysis and computational modelling. This resource advances functional group annotation by balancing chemical specificity and computational performance, supporting reproducible and scalable cheminformatics research.

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Publication Details

Journal
Journal of Cheminformatics
Published
2025-12-08
DOI
https://doi.org/10.1186/s13321-025-01136-8
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

SMARTS-RX: a SMARTS-based representation of chemical functions for reactivity analysis

Samuel Genheden, Eike Caldeweyher, Thierry Kogej, Mikhail Kabeshov et al.
Journal of Cheminformatics
Computational Drug Discovery Methods
article

SMARTS-RX: a SMARTS-based representation of chemical functions for reactivity analysis

Samuel Genheden, Eike Caldeweyher, Thierry Kogej, Mikhail Kabeshov, Christos Kannas
article en

Abstract

Chemical functional group annotation provides a mechanistically meaningful framework to interpret model outcomes and guide synthetic strategies. Here, we present SMARTS-RX-a curated, hierarchical ontology of 406 SMARTS-based functional group descriptors-designed to characterize chemically relevant and reactive functionalities in small molecules. SMARTS-RX achieves a balance between granularity and computational efficiency by focusing on functional groups central to pharmaceutical synthesis and medicinal chemistry. We describe the development of SMARTS-RX, including its systematic nomenclature and SMARTS encoding, which enable precise tracking of chemical environments. The utility of SMARTS-RX for mapping chemical reactivity is demonstrated through analyses of functional group distributions across major reaction types, using large-scale datasets from AstraZeneca's Electronic Lab Notebooks and Reaxys. Finally, we illustrate how this SMARTS library can be applied to guide building-block selection from commercial catalogues. A public GitHub repository has been created aiming for a continuous improvement of the current SMARTS_RX.Scientific Contribution: SMARTS-RX introduces a curated, hierarchical ontology of 406 SMARTS-based descriptors prioritizing pharmaceutical relevance and mechanistic interpretability. Distinct from prior efforts, SMARTS-RX encodes detailed chemical environments to improve reactivity mapping and feature extraction for both expert analysis and computational modelling. This resource advances functional group annotation by balancing chemical specificity and computational performance, supporting reproducible and scalable cheminformatics research.

Journal of CheminformaticsVol. 17(1)
AstraZeneca (Japan) (JP), AstraZeneca (Finland) (FI), AstraZeneca (Sweden) (SE)
Openalex Percentile: Top 62%
Computational Drug Discovery Methods
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