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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:

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy
Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, et al.
Chemical Science (2020) Vol. 11, Iss. 12, pp. 3316-3325
Open Access | Times Cited: 352

Showing 1-25 of 352 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
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
Igor V. Tetko, Pavel Karpov, Ruud van Deursen, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 296
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
Artificial intelligence in drug discovery: recent advances and future perspectives
José Jiménez-Luna, Francesca Grisoni, Nils Weskamp, et al.
Expert Opinion on Drug Discovery (2021) Vol. 16, Iss. 9, pp. 949-959
Open Access | Times Cited: 274
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
Computational planning of the synthesis of complex natural products
Barbara Mikulak-Klucznik, Patrycja Gołębiowska, Alison A. Bayly, et al.
Nature (2020) Vol. 588, Iss. 7836, pp. 83-88
Open Access | Times Cited: 210
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
Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 194
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Edward O. Pyzer‐Knapp, Jed W. Pitera, Peter Staar, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 185
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
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
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, et al.
Patterns (2022) Vol. 3, Iss. 10, pp. 100588-100588
Open Access | Times Cited: 143
Graph neural networks for automated de novo drug design
Jiacheng Xiong, Zhaoping Xiong, Kaixian Chen, et al.
Drug Discovery Today (2021) Vol. 26, Iss. 6, pp. 1382-1393
Closed Access | Times Cited: 134
Automation and computer-assisted planning for chemical synthesis
Yuning Shen, Julia E. Borowski, Melissa A. Hardy, et al.
Nature Reviews Methods Primers (2021) Vol. 1, Iss. 1
Closed Access | Times Cited: 128
Taking the leap between analytical chemistry and artificial intelligence: A tutorial review
Lucas B. Ayres, Federico J.V. Gómez, Jeb R. Linton, et al.
Analytica Chimica Acta (2021) Vol. 1161, pp. 338403-338403
Closed Access | Times Cited: 124
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
Ab Initio Machine Learning in Chemical Compound Space
Bing Huang, O. Anatole von Lilienfeld
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 10001-10036
Open Access | Times Cited: 121
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: 109
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
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
Michaël Moret, Irène Pachón-Angona, Leandro Cotos, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 85
polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Christopher Kuenneth, Rampi Ramprasad
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 73
Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments
Umit Volkan Ucak, Islambek Ashyrmamatov, Junsu Ko, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 69
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