Reference#

Use this page when citing SynRXN or tracing the upstream datasets and methods used throughout the benchmark inventory.

Primary SynRXN citations#

Cite the paper for the benchmark description and cite the exact Zenodo version for the data/software archive used in experiments.

Item	When to cite	Reference key
Scientific Data paper	Always cite this when SynRXN contributes to a publication.	`phan2026synrxn`
Zenodo version record	Cite the exact archived release used for data loading, manifests, and reproducible benchmarking.	`phan_synrxn_zenodo_v100` or the key exported by Zenodo

BibTeX#

@article{phan2026synrxn,
  title = {SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling},
  author = {Phan, Tieu-Long and Nguyen Song, Nhu-Ngoc and Stadler, Peter F.},
  journal = {Scientific Data},
  volume = {13},
  pages = {625},
  year = {2026},
  doi = {10.1038/s41597-026-07260-w},
  url = {https://www.nature.com/articles/s41597-026-07260-w}
}

Zenodo version BibTeX template#

Use Zenodo’s exported citation for the exact release you used. The example below shows the current documentation example and should be replaced when a newer data archive is used.

@misc{phan_synrxn_zenodo_v008,
  title = {synrxn: A Benchmarking Framework and Open Data Repository for Computer-Aided Synthesis Planning},
  author = {Phan, Tieu Long},
  publisher = {Zenodo},
  year = {2025},
  version = {v0.0.8},
  doi = {10.5281/zenodo.17672847},
  url = {https://doi.org/10.5281/zenodo.17672847}
}

Recommended citation record#

For reproducible benchmark reports, include both bibliographic and computational metadata.

Field	Example
Paper citation	`phan2026synrxn`
Zenodo citation	Version-specific DOI exported by Zenodo, for example `10.5281/zenodo.17672847`
Package version	`synrxn==1.0.0`
Data source	`zenodo` / `github` / `commit`
Data version	release number, tag, full commit SHA, or Zenodo version DOI
Dataset	`classification/schneider_b` or `property/b97xd3`
Split	published `split` column or generated split seed/configuration

Dataset and method references#

The bibliography below contains the primary SynRXN descriptor, the Zenodo release entry, and the upstream dataset, benchmark, and method references cited throughout the documentation, including the per-dataset sources listed in Data Records.

Bibliography#

[1]

Wojciech Jaworski, Sara Szymkuć, Barbara Mikulak-Klucznik, Katarzyna Piecuch, Tomasz Klucznik, Michał Kaźmierowski, Jakub Rydzewski, Anna Gambin, and Bartosz A. Grzybowski. Automatic mapping of atoms across both simple and complex chemical reactions. Nature Communications, 10:1434, 2019. doi:10.1038/s41467-019-09440-2.

[2]

Arkadii Lin, Natalia Dyubankova, Timur I. Madzhidov, Ramil I. Nugmanov, Jonas Verhoeven, Timur R. Gimadiev, Valentina A. Afonina, Zarina Ibragimova, Assima Rakhimbekova, Pavel Sidorov, Andrei Gedich, Rail Suleymanov, Ravil Mukhametgaleev, Joerg Wegner, Hugo Ceulemans, and Alexandre Varnek. Atom-to-atom mapping: a benchmarking study of popular mapping algorithms and consensus strategies. Molecular Informatics, 41(4):2100138, 2022. doi:10.1002/minf.202100138.

[3]

Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, and Vijay Pande. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Central Science, 3(10):1103–1113, 2017. doi:10.1021/acscentsci.7b00303.

[4]

Nadine Beier, Thomas Gatter, Jakob L. Andersen, and Peter F. Stadler. Computing double-pushout graph transformation rules and atom-to-atom maps from kegg rclass data. Algorithms for Molecular Biology, 21:3, 2026. doi:10.1186/s13015-025-00294-6.

[5]

Esther Heid, Daniel Probst, William H. Green, and Georg K. H. Madsen. Enzymemap: curation, validation and data-driven prediction of enzymatic reactions. Chemical Science, 14(48):14229–14242, 2023. doi:10.1039/D3SC02048G.

[6]

Shuan Chen, Sunggi An, Ramil Babazade, and Yousung Jung. Precise atom-to-atom mapping for organic reactions via human-in-the-loop machine learning. Nature Communications, 15:2250, 2024. doi:10.1038/s41467-024-46364-y.

[7]

Eleni E. Litsa, Matthew I. Peña, Mark Moll, George Giannakopoulos, George N. Bennett, and Lydia E. Kavraki. Machine learning guided atom mapping of metabolic reactions. Journal of Chemical Information and Modeling, 59(3):1121–1135, 2019. doi:10.1021/acs.jcim.8b00434.

[8]

Zishuo Zeng, Jin Guo, Jiao Jin, and Xiaozhou Luo. Claire: a contrastive learning-based predictor for ec number of chemical reactions. Journal of Cheminformatics, 17:2, 2025. doi:10.1186/s13321-024-00944-8.

[9]

Nadine Schneider, Daniel M. Lowe, Roger A. Sayle, and Gregory A. Landrum. Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity. Journal of Chemical Information and Modeling, 55(1):39–53, 2015. doi:10.1021/ci5006614.

[10]

Phuoc-Chung Van Nguyen, Van-Thinh To, Ngoc-Vi Nguyen Tran, Tieu-Long Phan, Tuyen Ngoc Truong, Thomas Gärtner, Daniel Merkle, and Peter F. Stadler. Syncat: molecule-level attention graph neural network for precise reaction classification. Digital Discovery, 5(1):241–253, 2026. doi:10.1039/D5DD00367A.

[11]

Tieu-Long Phan, Klaus Weinbauer, Marcos E. González Laffitte, Yingjie Pan, Daniel Merkle, Jakob L. Andersen, Rolf Fagerberg, Christoph Flamm, and Peter F. Stadler. Syntemp: efficient extraction of graph-based reaction rules from large-scale reaction databases. Journal of Chemical Information and Modeling, 65(6):2882–2896, 2025. doi:10.1021/acs.jcim.4c01795.

[12]

Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, and Jean-Louis Reymond. Mapping the space of chemical reactions using attention-based neural networks. Nature Machine Intelligence, 3:144–152, 2021. doi:10.1038/s42256-020-00284-w.

[13]

Colin A. Grambow, Lagnajit Pattanaik, and William H. Green. Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry. Scientific Data, 7:137, 2020. doi:10.1038/s41597-020-0460-4.

[14]

Colin A. Grambow, Lagnajit Pattanaik, and William H. Green. Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry. 2020. Dataset. doi:10.5281/zenodo.3715478.

[15]

Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green, and Charles J. McGill. Benchmark data for chemprop. 2023. Dataset. doi:10.5281/zenodo.8174268.

[16]

Esther Heid and William H. Green. Machine learning of reaction properties via learned representations of the condensed graph of reaction. Journal of Chemical Information and Modeling, 62(9):2101–2110, 2022. doi:10.1021/acs.jcim.1c00975.

[17]

Guido Falk von Rudorff, Stefan N. Heinen, Marco Bragato, and O. Anatole von Lilienfeld. Thousands of reactants and transition states for competing e2 and s$_\mathrm N$2 reactions. Machine Learning: Science and Technology, 1(4):045026, 2020. doi:10.1088/2632-2153/aba822.

[18]

Pierre L. Bhoorasingh, Belinda L. Slakman, Fariba Seyedzadeh Khanshan, Jason Y. Cain, and Richard H. West. Automated transition state theory calculations for high-throughput kinetics. The Journal of Physical Chemistry A, 121(37):6896–6904, 2017. doi:10.1021/acs.jpca.7b07361.

[19]

Hua Huang, Chetanya Pandya, Chunliang Liu, Nawal F. Al-Obaidi, Mengyi Wang, Lianqing Zheng, Sarah Toews Keating, Michelle Aono, James D. Love, Barbara Evans, Ronald D. Seidel, Brandi S. Hillerich, Scott J. Garforth, Steven C. Almo, Patrick S. Mariano, Debra Dunaway-Mariano, and Karen N. Allen. Panoramic view of a superfamily of phosphatases through substrate profiling. Proceedings of the National Academy of Sciences, 112(16):E1974–E1983, 2015. doi:10.1073/pnas.1423570112.

[20]

Sina Stocker, Gábor Csányi, Karsten Reuter, and Johannes T. Margraf. Machine learning in chemical reaction space. Nature Communications, 11:5505, 2020. doi:10.1038/s41467-020-19267-x.

[21]

Kjell Jorner, Tore Brinck, Per-Ola Norrby, and David Buttar. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies. Chemical Science, 12:1163–1175, 2021. doi:10.1039/D0SC04896H.

[22]

Ching Ching Lam and Jonathan M. Goodman. Every atom counts: predicting sites of reaction based on chemistry within two bonds. Digital Discovery, 3(9):1878–1888, 2024. doi:10.1039/D4DD00092G.

[23]

Jieyu Lu and Yingkai Zhang. Unified deep learning model for multitask reaction predictions with explanation. Journal of Chemical Information and Modeling, 62(6):1376–1387, 2022. doi:10.1021/acs.jcim.1c01467.

[24]

Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi Jaakkola. Predicting organic reaction outcomes with weisfeiler–lehman networks. In Advances in Neural Information Processing Systems, volume 30, 2604–2613. 2017. URL: https://papers.nips.cc/paper/6854-predicting-organic-reaction-outcomes-with-weisfeiler-lehman-network.

[25]

Tieu-Long Phan, Nhu-Ngoc Nguyen Song, and Peter F. Stadler. Synrxn: an open benchmark and curated dataset for computational reaction modeling. Scientific Data, 13:625, 2026. URL: https://www.nature.com/articles/s41597-026-07260-w, doi:10.1038/s41597-026-07260-w.

[26]

Boris Weisfeiler and Andrei Leman. The reduction of a graph to canonical form and the algebra which appears therein. Nauchno-Technicheskaya Informatsia, Series 2, 9:12–16, 1968.

[27]

Tieu Long Phan. Synrxn: a benchmarking framework and open data repository for computer-aided synthesis planning. 2025. URL: https://doi.org/10.5281/zenodo.17672847, doi:10.5281/zenodo.17672847.

[28]

Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. A software package for chemically inspired graph transformation. In Graph Transformation: Proceedings of the 9th International Conference on Graph Transformation, volume 9761 of Lecture Notes in Computer Science, 73–88. Springer, 2016. doi:10.1007/978-3-319-40530-8_5.