VizChemoton: Visualization of Quantum Chemical Reaction Networks and Their Cheminformatics Features
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.
Authors
- Diego Garay‐Ruiz (ORCID: https://orcid.org/0000-0003-0744-0562)
- Enric Petrus (ORCID: https://orcid.org/0000-0002-8022-3153)
- Thomas B. Hofstetter (ORCID: https://orcid.org/0000-0003-1906-367X)
- Thomas Weymuth (ORCID: https://orcid.org/0000-0001-7102-7022)
- Markus Reiher (ORCID: https://orcid.org/0000-0002-9508-1565)
Institutions
- ETH Zurich (CH)
- Institute of Micro and Nanotechnology (ES)
- Swiss Federal Institute of Aquatic Science and Technology (CH)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2026-09-15
- DOI
- https://doi.org/10.1021/acs.jcim.6c02689
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00