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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:

Mapping the space of chemical reactions using attention-based neural networks
Philippe Schwaller, Daniel Probst, Alain C. Vaucher, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 2, pp. 144-152
Open Access | Times Cited: 183

Showing 1-25 of 183 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 582
Geometric deep learning on molecular representations
Kenneth Atz, Francesca Grisoni, Gisbert Schneider
Nature Machine Intelligence (2021) Vol. 3, Iss. 12, pp. 1023-1032
Closed Access | Times Cited: 229
Extraction of organic chemistry grammar from unsupervised learning of chemical reactions
Philippe Schwaller, Benjamin Hoover, Jean‐Louis Reymond, et al.
Science Advances (2021) Vol. 7, Iss. 15
Open Access | Times Cited: 205
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentín Vassilev-Galindo, Bingqing Cheng, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9816-9872
Open Access | Times Cited: 175
Prediction of chemical reaction yields using deep learning
Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, et al.
Machine Learning Science and Technology (2021) Vol. 2, Iss. 1, pp. 015016-015016
Open Access | Times Cited: 165
Machine Learning for Chemical Reactivity: The Importance of Failed Experiments
Felix Strieth‐Kalthoff, Frederik Sandfort, Marius Kühnemund, et al.
Angewandte Chemie International Edition (2022) Vol. 61, Iss. 29
Closed Access | Times Cited: 140
Explainable machine learning in materials science
Xiaoting Zhong, Brian Gallagher, Shusen Liu, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 137
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
Controllable protein design with language models
Noelia Ferruz, Birte Höcker
Nature Machine Intelligence (2022) Vol. 4, Iss. 6, pp. 521-532
Open Access | Times Cited: 122
Reaction classification and yield prediction using the differential reaction fingerprint DRFP
Daniel Probst, Philippe Schwaller, Jean‐Louis Reymond
Digital Discovery (2022) Vol. 1, Iss. 2, pp. 91-97
Open Access | Times Cited: 106
Evaluation guidelines for machine learning tools in the chemical sciences
Andreas Bender, Nadine Schneider, Marwin Segler, et al.
Nature Reviews Chemistry (2022) Vol. 6, Iss. 6, pp. 428-442
Closed Access | Times Cited: 95
Augmenting large language models with chemistry tools
Andres M. Bran, Sam Cox, Oliver Schilter, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 5, pp. 525-535
Open Access | Times Cited: 94
Biocatalysed synthesis planning using data-driven learning
Daniel Probst, Matteo Manica, Yves Gaëtan Nana Teukam, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 89
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
MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
Zhonglin Cao, Rishikesh Magar, Yuyang Wang, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 5, pp. 2958-2967
Open Access | Times Cited: 70
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
Regression Transformer enables concurrent sequence regression and generation for molecular language modelling
Jannis Born, Matteo Manica
Nature Machine Intelligence (2023) Vol. 5, Iss. 4, pp. 432-444
Open Access | Times Cited: 65
Battery safety: Machine learning-based prognostics
Jingyuan Zhao, Xuning Feng, Quanquan Pang, et al.
Progress in Energy and Combustion Science (2024) Vol. 102, pp. 101142-101142
Open Access | Times Cited: 40
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
Predicting enzymatic reactions with a molecular transformer
David Kreutter, Philippe Schwaller, Jean‐Louis Reymond
Chemical Science (2021) Vol. 12, Iss. 25, pp. 8648-8659
Open Access | Times Cited: 60
Chemputation and the Standardization of Chemical Informatics
Alexander Hammer, Artem I. Leonov, Nicola L. Bell, et al.
JACS Au (2021) Vol. 1, Iss. 10, pp. 1572-1587
Open Access | Times Cited: 60
Machine learning activation energies of chemical reactions
Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 12, Iss. 4
Open Access | Times Cited: 55
Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network
Dario Caramelli, Jarosław M. Granda, S. Hessam M. Mehr, et al.
ACS Central Science (2021) Vol. 7, Iss. 11, pp. 1821-1830
Open Access | Times Cited: 53
Unified Deep Learning Model for Multitask Reaction Predictions with Explanation
Jieyu Lü, Yingkai Zhang
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 6, pp. 1376-1387
Open Access | Times Cited: 52
Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings
Jules Schleinitz, Maxime Langevin, Yanis Smail, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 32, pp. 14722-14730
Open Access | Times Cited: 46
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