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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:

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, et al.
ACS Central Science (2019) Vol. 5, Iss. 9, pp. 1572-1583
Open Access | Times Cited: 655

Showing 1-25 of 655 citing articles:

Drug discovery with explainable artificial intelligence
José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider
Nature Machine Intelligence (2020) Vol. 2, Iss. 10, pp. 573-584
Open Access | Times Cited: 692
A survey of transformers
Tianyang Lin, Yuxin Wang, Xiangyang Liu, et al.
AI Open (2022) Vol. 3, pp. 111-132
Open Access | Times Cited: 596
Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 3, pp. 279-287
Closed Access | Times Cited: 405
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
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Lifan Chen, Xiaoqin Tan, Dingyan Wang, et al.
Bioinformatics (2020) Vol. 36, Iss. 16, pp. 4406-4414
Open Access | Times Cited: 305
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: 299
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
The Role of Machine Learning in the Understanding and Design of Materials
Seyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit
Journal of the American Chemical Society (2020) Vol. 142, Iss. 48, pp. 20273-20287
Open Access | Times Cited: 284
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
Emerging materials intelligence ecosystems propelled by machine learning
Rohit Batra, Le Song, Rampi Ramprasad
Nature Reviews Materials (2020) Vol. 6, Iss. 8, pp. 655-678
Closed Access | Times Cited: 242
MolGPT: Molecular Generation Using a Transformer-Decoder Model
Viraj Bagal, Rishal Aggarwal, P. K. Vinod, et al.
Journal of Chemical Information and Modeling (2021) Vol. 62, Iss. 9, pp. 2064-2076
Closed Access | Times Cited: 236
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
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
Seyone Chithrananda, Gabriel Grand, Bharath Ramsundar
arXiv (Cornell University) (2020)
Open Access | Times Cited: 220
AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning
Samuel Genheden, Amol Thakkar, Veronika Chadimová, et al.
Journal of Cheminformatics (2020) Vol. 12, Iss. 1
Open Access | Times Cited: 216
Machine learning the ropes: principles, applications and directions in synthetic chemistry
Felix Strieth‐Kalthoff, Frederik Sandfort, Marwin Segler, et al.
Chemical Society Reviews (2020) Vol. 49, Iss. 17, pp. 6154-6168
Closed Access | Times Cited: 215
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: 206
Transformer-CNN: Swiss knife for QSAR modeling and interpretation
Pavel Karpov, Guillaume Godin, Igor V. Tetko
Journal of Cheminformatics (2020) Vol. 12, Iss. 1
Open Access | Times Cited: 203
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
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Maarten R. Dobbelaere, Pieter Plehiers, Ruben Van de Vijver, et al.
Engineering (2021) Vol. 7, Iss. 9, pp. 1201-1211
Open Access | Times Cited: 191
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Thomas J. Struble, Juan C. Alvarez, Scott P. Brown, et al.
Journal of Medicinal Chemistry (2020) Vol. 63, Iss. 16, pp. 8667-8682
Open Access | Times Cited: 189
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
A Brief Introduction to Chemical Reaction Optimization
Connor J. Taylor, Alexander Pomberger, Kobi Felton, et al.
Chemical Reviews (2023) Vol. 123, Iss. 6, pp. 3089-3126
Open Access | Times Cited: 183
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: 176
A review of molecular representation in the age of machine learning
Daniel Wigh, Jonathan M. Goodman, Alexei A. Lapkin
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 176
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