Molecule benchmarks (QM9, ZINC, MUTAG)

data category: Graph learning benchmarks · 0 papers in the last 7 days

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Citation, molecule and other graphs used to compare graph neural networks.

Names searched for: QM9, ZINC250k, MUTAG, MoleculeNet, TUDataset

Key findings#

From Nov 2023 to Oct 2026, 102 archived papers mentioned Molecule benchmarks (QM9, ZINC, MUTAG) in their title, abstract, keywords or full text, most of them in Jan 2025 (6).
No paper of the tracked journals mentioned Molecule benchmarks (QM9, ZINC, MUTAG) in the last 30 days (2026-10-07 to 2026-11-05).
From 2017 to 2026 (so far), Molecule benchmarks (QM9, ZINC, MUTAG) was mentioned in 108 papers in the tracked journals, most of them in AI (105), information systems (2) and higher education and research (1).
31 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were Hong Kong Polytechnic University (6), Beihang University (4) and Shanghai Jiao Tong University (4).
The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (15), IEEE Transactions on Knowledge and Data Engineering (13) and Neural Networks (12).
The most-cited of them is “Augmenting large language models with chemistry tools” (Nature Machine Intelligence, 2024), with 795 citations.
Papers use Molecule benchmarks (QM9, ZINC, MUTAG) most often with IMDb (28), Open Graph Benchmark (25) and Reddit (20); it has 15 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts, keywords and full texts)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	37	35	30	102

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2023	2024	2025	2026 (so far)
AI	13	36	29	27
Information Systems	0	0	1	1
Higher Education and Research	0	0	1	0
All categories	13	36	31	28

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	IEEE Transactions on Pattern Analysis and Machine Intelligence	15
2	IEEE Transactions on Knowledge and Data Engineering	13
3	Neural Networks	12
4	Nature Machine Intelligence	10
5	IEEE Transactions on Neural Networks and Learning Systems	8
6	Machine Learning	6
7	Pattern Recognition	5
8	Pattern Recognition Letters	4
9	Neural Computing and Applications	4
10	IEEE Transactions on Artificial Intelligence	4

Institutions with the most papers, since 2023
#	Institution	Papers
1	Hong Kong Polytechnic University	6
2	Beihang University	4
3	Shanghai Jiao Tong University	4
4	University of Science and Technology of China	4
5	Chinese Academy of Sciences	3
6	Chinese University of Hong Kong	3
7	Harbin Institute of Technology	3
8	Jilin University	3
9	Macquarie University	3
10	Nanyang Technological University	3

Countries with the most papers, since 2023
#	Country	Papers
1	China	57
2	United States	24
3	Hong Kong	14
4	United Kingdom	13
5	Italy	11
6	Australia	8
7	Singapore	7
8	Switzerland	5
9	France	4
10	South Korea	4

Keywords with the most papers, since 2023
#	Keyword	Papers
1	graph neural networks	55
2	graph classification	24
3	graph representation learning	13
4	node classification	11
5	interpretability	10
6	molecular property prediction	10
7	over-smoothing	10
8	drug discovery	8
9	message passing	8
10	graph convolutional networks	7

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	795	26.6	Augmenting large language models with chemistry tools	Nature Machine Intelligence	2024-05-08	Andres M Bran, Sam Cox, Oliver Tobias Schilter et al.
2	345	10.7	A Comprehensive Survey on Deep Graph Representation Learning	Neural Networks	2024-02-27	Wei Ju, Zheng Fang, Yiyang Gu et al.
3	183	5.3	Multi-modal molecule structure–text model for text-based retrieval and editing	Nature Machine Intelligence	2023-12-18	Shengchao Liu, Weili Nie, Chengpeng Wang et al.
4	161	9.8	A framework to evaluate machine learning crystal stability predictions	Nature Machine Intelligence	2025-06-23	Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner et al.
5	125	3.4	Neural scaling of deep chemical models	Nature Machine Intelligence	2023-10-23	Nathan C. Frey, Ryan Soklaski, Simon Axelrod et al.

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Used together with#

Papers use Molecule benchmarks (QM9, ZINC, MUTAG) most often with IMDb (28), Open Graph Benchmark (25) and Reddit (20); it has 15 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok. The whole network →

What papers use Molecule benchmarks (QM9, ZINC, MUTAG) together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
IMDb	Dataset	28	52.8
Open Graph Benchmark	Dataset	25	120.4
Reddit	Dataset	20	14.6
MNIST / Fashion-MNIST	Dataset	11	4.4
Cora / CiteSeer / PubMed (Planetoid)	Dataset	9	35.3
ImageNet	Dataset	9	1.7
CIFAR-10 / CIFAR-100	Dataset	7	2.2
Facebook	Dataset	5	0.7
Twitter / X	Dataset	4	0.4
Wikipedia	Dataset	4	1.7
Amazon reviews	Dataset	3	5.6
Hugging Face Hub	Dataset	3	2.9

Cite this page

Scholar4AI. (2026). Molecule benchmarks (QM9, ZINC, MUTAG). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/molecule-benchmarks-qm9-zinc-mutag

@misc{scholar4ai-datasets-molecule-benchmarks-qm9-zinc-mutag,
  author = {{Scholar4AI}},
  title = {Molecule benchmarks (QM9, ZINC, MUTAG)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/molecule-benchmarks-qm9-zinc-mutag}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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