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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors
Yanfei Guan, Connor W. Coley, Haoyang Wu, et al.
Chemical Science (2020) Vol. 12, Iss. 6, pp. 2198-2208
Open Access | Times Cited: 101

Showing 1-25 of 101 citing articles:

Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, et al.
Communications Materials (2022) Vol. 3, Iss. 1
Open Access | Times Cited: 291
Organic reactivity from mechanism to machine learning
Kjell Jorner, Anna Tomberg, Christoph Bauer, et al.
Nature Reviews Chemistry (2021) Vol. 5, Iss. 4, pp. 240-255
Closed Access | Times Cited: 135
Chemprop: A Machine Learning Package for Chemical Property Prediction
Esther Heid, Kevin P. Greenman, Yunsie Chung, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 1, pp. 9-17
Open Access | Times Cited: 124
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 7, pp. 3273-3284
Open Access | Times Cited: 111
Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 87
Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability
Thijs Stuyver, Connor W. Coley
The Journal of Chemical Physics (2022) Vol. 156, Iss. 8
Open Access | Times Cited: 69
Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Zhengkai Tu, Thijs Stuyver, Connor W. Coley
Chemical Science (2022) Vol. 14, Iss. 2, pp. 226-244
Open Access | Times Cited: 66
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction
Jin Li, Naiteng Wu, Jian Zhang, et al.
Nano-Micro Letters (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 56
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 4, pp. 340-350
Open Access | Times Cited: 53
Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning
Li‐Cheng Xu, Johanna Frey, Xiaoyan Hou, et al.
Nature Synthesis (2023) Vol. 2, Iss. 4, pp. 321-330
Closed Access | Times Cited: 40
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 15
Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network
Yanfei Guan, S. V. Shree Sowndarya, Liliana C. Gallegos, et al.
Chemical Science (2021) Vol. 12, Iss. 36, pp. 12012-12026
Open Access | Times Cited: 86
Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction
Esther Heid, William H. Green
Journal of Chemical Information and Modeling (2021) Vol. 62, Iss. 9, pp. 2101-2110
Open Access | Times Cited: 77
Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
Dávid Péter Kovács, William McCorkindale, Alpha A. Lee
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 74
Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy
Kevin Spiekermann, Lagnajit Pattanaik, William H. Green
The Journal of Physical Chemistry A (2022) Vol. 126, Iss. 25, pp. 3976-3986
Open Access | Times Cited: 52
Machine-Learning-Guided Discovery of Electrochemical Reactions
Andrew F. Zahrt, Yiming Mo, Kakasaheb Y. Nandiwale, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 49, pp. 22599-22610
Open Access | Times Cited: 45
Predicting reaction conditions from limited data through active transfer learning
Eunjae Shim, Joshua Kammeraad, Ziping Xu, et al.
Chemical Science (2022) Vol. 13, Iss. 22, pp. 6655-6668
Open Access | Times Cited: 36
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
David F. Nippa, Kenneth Atz, Remo Hohler, et al.
Nature Chemistry (2023) Vol. 16, Iss. 2, pp. 239-248
Open Access | Times Cited: 32
Dataset Design for Building Models of Chemical Reactivity
Priyanka Raghavan, Brittany C. Haas, Madeline E. Ruos, et al.
ACS Central Science (2023) Vol. 9, Iss. 12, pp. 2196-2204
Open Access | Times Cited: 31
Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Sukriti Singh, Raghavan B. Sunoj
Accounts of Chemical Research (2023) Vol. 56, Iss. 3, pp. 402-412
Closed Access | Times Cited: 28
Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential
Thijs Stuyver, Connor W. Coley
Chemistry - A European Journal (2023) Vol. 29, Iss. 28
Open Access | Times Cited: 23
Deep learning metal complex properties with natural quantum graphs
Hannes Kneiding, Ruslan Lukin, Lucas Lang, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 618-633
Open Access | Times Cited: 21
Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
Yunsie Chung, William H. Green
Chemical Science (2024) Vol. 15, Iss. 7, pp. 2410-2424
Open Access | Times Cited: 11
Predictive Minisci late stage functionalization with transfer learning
Emma King‐Smith, Felix A. Faber, Usa Reilly, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 10
AiZynth impact on medicinal chemistry practice at AstraZeneca
Jason D. Shields, Rachel Howells, Gillian M. Lamont, et al.
RSC Medicinal Chemistry (2024) Vol. 15, Iss. 4, pp. 1085-1095
Closed Access | Times Cited: 10
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