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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:

Rethinking drug design in the artificial intelligence era
Petra Schneider, W. Patrick Walters, Alleyn T. Plowright, et al.
Nature Reviews Drug Discovery (2019) Vol. 19, Iss. 5, pp. 353-364
Open Access | Times Cited: 646

Showing 1-25 of 646 citing articles:

Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Rohan Gupta, Devesh Srivastava, Mehar Sahu, et al.
Molecular Diversity (2021) Vol. 25, Iss. 3, pp. 1315-1360
Open Access | Times Cited: 889
Towards the sustainable discovery and development of new antibiotics
Marcus Miethke, Marco Pieroni, Tilmann Weber, et al.
Nature Reviews Chemistry (2021) Vol. 5, Iss. 10, pp. 726-749
Open Access | Times Cited: 797
Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte, et al.
IEEE Access (2020) Vol. 8, pp. 42200-42216
Open Access | Times Cited: 747
Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review
Assunta Di Vaio, Rosa Palladino, Rohail Hassan, et al.
Journal of Business Research (2020) Vol. 121, pp. 283-314
Closed Access | Times Cited: 724
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: 709
Computational approaches streamlining drug discovery
Anastasiia Sadybekov, Vsevolod Katritch
Nature (2023) Vol. 616, Iss. 7958, pp. 673-685
Open Access | Times Cited: 541
Challenges and advances in clinical applications of mesenchymal stromal cells
Tian Zhou, Zenan Yuan, Jianyu Weng, et al.
Journal of Hematology & Oncology (2021) Vol. 14, Iss. 1
Open Access | Times Cited: 450
Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Lalitkumar K. Vora, Amol D. Gholap, Keshava Jetha, et al.
Pharmaceutics (2023) Vol. 15, Iss. 7, pp. 1916-1916
Open Access | Times Cited: 393
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, et al.
Chemical Reviews (2021) Vol. 122, Iss. 12, pp. 10899-10969
Open Access | Times Cited: 298
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: 296
Mechanism of baricitinib supports artificial intelligence‐predicted testing in COVID ‐19 patients
Justin Stebbing, Venkatesh Krishnan, Stephanie de Bono, et al.
EMBO Molecular Medicine (2020) Vol. 12, Iss. 8
Open Access | Times Cited: 248
Phenotypic drug discovery: recent successes, lessons learned and new directions
Fabien Vincent, Arsenio Nueda, Jonathan Lee, et al.
Nature Reviews Drug Discovery (2022) Vol. 21, Iss. 12, pp. 899-914
Open Access | Times Cited: 191
The strategies and techniques of drug discovery from natural products
Li Zhang, Junke Song, Ling-Lei Kong, et al.
Pharmacology & Therapeutics (2020) Vol. 216, pp. 107686-107686
Closed Access | Times Cited: 176
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
Linlin Zhao, Heather L. Ciallella, Lauren M. Aleksunes, et al.
Drug Discovery Today (2020) Vol. 25, Iss. 9, pp. 1624-1638
Open Access | Times Cited: 173
Learning Molecular Representations for Medicinal Chemistry
Kangway V. Chuang, Laura M. Gunsalus, Michael J. Keiser
Journal of Medicinal Chemistry (2020) Vol. 63, Iss. 16, pp. 8705-8722
Open Access | Times Cited: 172
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
Moe Elbadawi, Brais Muñiz Castro, Francesca K. H. Gavins, et al.
International Journal of Pharmaceutics (2020) Vol. 590, pp. 119837-119837
Open Access | Times Cited: 171
Machine learning models for drug–target interactions: current knowledge and future directions
Sofia D’Souza, K. V. Prema, S. Balaji
Drug Discovery Today (2020) Vol. 25, Iss. 4, pp. 748-756
Closed Access | Times Cited: 168
Exploring different approaches to improve the success of drug discovery and development projects: a review
Geoffrey Kabue Kiriiri, Peter Njogu, Alex Mwangi
Future Journal of Pharmaceutical Sciences (2020) Vol. 6, Iss. 1
Open Access | Times Cited: 156
Accelerating antibiotic discovery through artificial intelligence
Marcelo C. R. Melo, Jacqueline R. M. A. Maasch, César de la Fuente‐Núñez
Communications Biology (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 149
Natural product drug discovery in the artificial intelligence era
Fernanda I. Saldívar‐González, Victor Daniel Aldas-Bulos, José L. Medina‐Franco, et al.
Chemical Science (2021) Vol. 13, Iss. 6, pp. 1526-1546
Open Access | Times Cited: 135
Deep generative molecular design reshapes drug discovery
Xiangxiang Zeng, Fei Wang, Yuan Luo, et al.
Cell Reports Medicine (2022) Vol. 3, Iss. 12, pp. 100794-100794
Open Access | Times Cited: 132
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu, et al.
Neural Networks (2024) Vol. 173, pp. 106207-106207
Open Access | Times Cited: 130
Macrocycles in Drug Discovery─Learning from the Past for the Future
Diego García Jiménez, Vasanthanathan Poongavanam, Jan Kihlberg
Journal of Medicinal Chemistry (2023) Vol. 66, Iss. 8, pp. 5377-5396
Open Access | Times Cited: 128
Intelligent Computing: The Latest Advances, Challenges, and Future
Shiqiang Zhu, Ting Yu, Tao Xu, et al.
Intelligent Computing (2023) Vol. 2
Open Access | Times Cited: 127
Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation
Harini Narayanan, Fabian Dingfelder, Alessandro Butté, et al.
Trends in Pharmacological Sciences (2021) Vol. 42, Iss. 3, pp. 151-165
Closed Access | Times Cited: 125
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