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Peer-reviewedCC BYJournal of chemical information and modeling

VizChemoton: Visualization of Quantum Chemical Reaction Networks and Their Cheminformatics Features.

  • Enric Petrus1
  • Diego Garay-Ruiz2
  • Thomas Weymuth3
  • Markus Reiher3
  • Thomas B Hofstetter1,4
4 affiliations
  1. 1Eawag, Swiss Federal Institute of Aquatic Science and Technology , 8600Dübendorf, Switzerland.
  2. 2Instituto de Micro y Nanotecnologıa, IMN-CNM, CSIC (CEI UAM+CSIC) , Isaac Newton, 8, Tres Cantos, MadridE-28760, Spain.
  3. 3Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093Zürich, Switzerland.
  4. 4Institute of Biogeochemistry and Pollutant Dynamics (IBP), ETH Zürich, 8092Zürich, Switzerland.
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Abstract

Automated explorations of chemical reaction networks (CRNs) guided by quantum chemistry (QC) calculations are undergoing rapid development that enables numerous applications. However, CRNs are accessible primarily through specialized quantum chemistry software without interface for cheminformatic exploitation of the generated data. Here, we present VizChemoton, an open-source module within the reaction exploration framework Software for Chemical Interaction Networks (SCINE). VizChemoton enables visualization of CRNs and serves as an interface for cheminformatics applications based on conversion of molecular structures that accounts for radicals, zwitterions, and molecular complexes into string-based representations via RDKit mol objects. The conversion performance exceeded 90% of QC-generated compounds based on the evaluation of four CRNs constructed with density functional theory and extended tight-binding methods. We find that compounds absent from public structural databases correspond primarily to molecular complexes and unstable reaction intermediates. All essential QC and associated cheminformatics data are distributed in interoperable CSV and JSON formats to support downstream machine learning applications whereas reaction networks are readily visualized through a browser-based standalone HTML interface.

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Cite

BibTeXRIS
BibTeX
@article{petrus2026vizchemoton,
  title = {{VizChemoton: Visualization of Quantum Chemical Reaction Networks and Their Cheminformatics Features.}},
  author = {Enric Petrus and Diego Garay-Ruiz and Thomas Weymuth and Markus Reiher and Thomas B Hofstetter},
  journal = {Journal of chemical information and modeling},
  year = {2026},
  month = sep,
  doi = {10.1021/acs.jcim.6c02689},
  url = {https://europepmc.org/article/MED/42906060},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - VizChemoton: Visualization of Quantum Chemical Reaction Networks and Their Cheminformatics Features.
AU  - Enric Petrus
AU  - Diego Garay-Ruiz
AU  - Thomas Weymuth
AU  - Markus Reiher
AU  - Thomas B Hofstetter
PY  - 2026
DA  - 2026/09/01
JO  - Journal of chemical information and modeling
DO  - 10.1021/acs.jcim.6c02689
UR  - https://europepmc.org/article/MED/42906060
AB  - Automated explorations of chemical reaction networks (CRNs) guided by quantum chemistry (QC) calculations are undergoing rapid development that enables numerous applications. However, CRNs are accessible primarily through specialized quantum chemistry software without interface for cheminformatic exploitation of the generated data. Here, we present VizChemoton, an open-source module within the reaction exploration framework Software for Chemical Interaction Networks (SCINE). VizChemoton enables visualization of CRNs and serves as an interface for cheminformatics applications based on conversion of molecular structures that accounts for radicals, zwitterions, and molecular complexes into string-based representations via RDKit mol objects. The conversion performance exceeded 90% of QC-generated compounds based on the evaluation of four CRNs constructed with density functional theory and extended tight-binding methods. We find that compounds absent from public structural databases correspond primarily to molecular complexes and unstable reaction intermediates. All essential QC and associated cheminformatics data are distributed in interoperable CSV and JSON formats to support downstream machine learning applications whereas reaction networks are readily visualized through a browser-based standalone HTML interface.
SN  - 1549-9596
SN  - 1549-960X
ER  -