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:

A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W. Coley, Wengong Jin, Luke Rogers, et al.
Chemical Science (2018) Vol. 10, Iss. 2, pp. 370-377
Open Access | Times Cited: 550

Showing 1-25 of 550 citing articles:

A Deep Learning Approach to Antibiotic Discovery
Jonathan Stokes, Kevin Yang, Kyle Swanson, et al.
Cell (2020) Vol. 180, Iss. 4, pp. 688-702.e13
Open Access | Times Cited: 1506

Artificial intelligence: A powerful paradigm for scientific research
Yongjun Xu, Xin Liu, Xin Cao, et al.
The Innovation (2021) Vol. 2, Iss. 4, pp. 100179-100179
Open Access | Times Cited: 825

A robotic platform for flow synthesis of organic compounds informed by AI planning
Connor W. Coley, Dale A. Thomas, Justin A. M. Lummiss, et al.
Science (2019) Vol. 365, Iss. 6453
Closed Access | Times Cited: 820

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

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

Deep Learning in Chemistry
Adam C. Mater, Michelle L. Coote
Journal of Chemical Information and Modeling (2019) Vol. 59, Iss. 6, pp. 2545-2559
Closed Access | Times Cited: 498

Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 2, pp. 1-96
Open Access | Times Cited: 437

Molecular representations in AI-driven drug discovery: a review and practical guide
Laurianne David, Amol Thakkar, Rocío Mercado, et al.
Journal of Cheminformatics (2020) Vol. 12, Iss. 1
Open Access | Times Cited: 384

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

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

A review on machine learning approaches and trends in drug discovery
Paula Carracedo-Reboredo, José Liñares-Blanco, Nereida Rodríguez-Fernández, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 4538-4558
Open Access | Times Cited: 295

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 Synthesizability of Molecules Proposed by Generative Models
Wenhao Gao, Connor W. Coley
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 5714-5723
Open Access | Times Cited: 288

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

Constrained Bayesian optimization for automatic chemical design using variational autoencoders
Ryan‐Rhys Griffiths, José Miguel Hernández-Lobato
Chemical Science (2019) Vol. 11, Iss. 2, pp. 577-586
Open Access | Times Cited: 268

Synthetic organic chemistry driven by artificial intelligence
A. Filipa Almeida, Rui Moreira, Tiago Rodrigues
Nature Reviews Chemistry (2019) Vol. 3, Iss. 10, pp. 589-604
Open Access | Times Cited: 254

Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot
Robert W. Epps, Michael Bowen, Amanda A. Volk, et al.
Advanced Materials (2020) Vol. 32, Iss. 30
Closed Access | Times Cited: 250

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

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

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

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development
Arash Keshavarzi Arshadi, Julia Webb, Milad Salem, et al.
Frontiers in Artificial Intelligence (2020) Vol. 3
Open Access | Times Cited: 209

BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules
Tzyy-Shyang Lin, Connor W. Coley, Hidenobu Mochigase, et al.
ACS Central Science (2019) Vol. 5, Iss. 9, pp. 1523-1531
Open Access | Times Cited: 206

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

Polymer informatics: Current status and critical next steps
Lihua Chen, Ghanshyam Pilania, Rohit Batra, et al.
Materials Science and Engineering R Reports (2020) Vol. 144, pp. 100595-100595
Open Access | Times Cited: 200

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