---
title: "Chemical reactions from US patents (1976-Sep2016)"
description: "6 papers in the tracked journals link to the figshare dataset Chemical reactions from US patents (1976-Sep2016) in their Crossref records, the first in 2019; most of them (3) in 2021."
url: https://scholar4ai.com/code/figshare-5104873
markdown_url: https://scholar4ai.com/code/figshare-5104873.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to the figshare dataset Chemical reactions from US patents (1976-Sep2016) in their Crossref records, the first in 2019; most of them (3) in 2021. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# Chemical reactions from US patents (1976-Sep2016): papers linking to it

Research papers in the 480 tracked journals that link to Chemical reactions from US patents (1976-Sep2016) (https://doi.org/10.6084/m9.figshare.5104873), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- figshare
- Dataset
- 2017
- https://doi.org/10.6084/m9.figshare.5104873

All repositories: https://scholar4ai.com/code.md

## Key findings

- 6 papers in the tracked journals link to the figshare dataset Chemical reactions from US patents (1976-Sep2016) in their Crossref records, the first in 2019; most of them (3) in 2021.
- By category: AI (6); the journals with the most are Nature Machine Intelligence (6).
- 4 of the papers build on one another: a later paper cites an earlier one that also links Chemical reactions from US patents (1976-Sep2016).

## By year

*Papers linking to Chemical reactions from US patents (1976-Sep2016) by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2021 | 3 |
| 2023 | 2 |

## Journals

*Journals whose papers link to Chemical reactions from US patents (1976-Sep2016) most*

| Journal | Papers |
|---|---:|
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 6 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Mapping the space of chemical reactions using attention-based neural networks](https://doi.org/10.1038/s42256-020-00284-w) (Nature Machine Intelligence, 2021)
  - [Reusability report: Learning the language of synthetic methods used in medicinal chemistry](https://doi.org/10.1038/s42256-021-00367-2) (Nature Machine Intelligence, 2021) — cites the paper above
  - [Regression Transformer enables concurrent sequence regression and generation for molecular language modelling](https://doi.org/10.1038/s42256-023-00639-z) (Nature Machine Intelligence, 2023) — cites the paper above
  - [Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model](https://doi.org/10.1038/s42256-023-00764-9) (Nature Machine Intelligence, 2023) — cites the paper above

## Papers

*Papers linking to Chemical reactions from US patents (1976-Sep2016), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-01-28 | [Mapping the space of chemical reactions using attention-based neural networks](https://doi.org/10.1038/s42256-020-00284-w) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Philippe Schwaller, Daniel Probst, Alain C. Vaucher et al. | 362 | cites it |
| 2023-04-06 | [Regression Transformer enables concurrent sequence regression and generation for molecular language modelling](https://doi.org/10.1038/s42256-023-00639-z) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Jannis Born, Matteo Manica | 144 | cites it |
| 2019-07-01 | [Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis](https://doi.org/10.1038/s42256-019-0067-7) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Alexander L. Button, Daniel Merk, Jan A. Hiss et al. | 103 | cites it |
| 2021-03-29 | [Unassisted noise reduction of chemical reaction datasets](https://doi.org/10.1038/s42256-021-00319-w) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Alessandra Toniato, Philippe Schwaller, Antonio Cardinale et al. | 62 | cites it |
| 2023-12-05 | [Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model](https://doi.org/10.1038/s42256-023-00764-9) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Bo Qiang, Yiran Zhou, Yuheng Ding et al. | 28 | cites it |
| 2021-07-05 | [Reusability report: Learning the language of synthetic methods used in medicinal chemistry](https://doi.org/10.1038/s42256-021-00367-2) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Jon Paul Janet, Anna Tomberg, Jonas Boström | 4 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). Chemical reactions from US patents (1976-Sep2016): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/figshare-5104873

```bibtex
@misc{scholar4ai-code-figshare-5104873,
  author = {{Scholar4AI}},
  title = {Chemical reactions from US patents (1976-Sep2016): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/figshare-5104873}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).