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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:

From machine learning to deep learning: progress in machine intelligence for rational drug discovery
Lu Zhang, Jianjun Tan, Dan Han, et al.
Drug Discovery Today (2017) Vol. 22, Iss. 11, pp. 1680-1685
Closed Access | Times Cited: 654

Showing 1-25 of 654 citing articles:

The rise of artificial intelligence in healthcare applications
Adam Bohr, Kaveh Memarzadeh
Elsevier eBooks (2020), pp. 25-60
Closed Access | Times Cited: 1033
Artificial intelligence in drug discovery and development
Debleena Paul, Gaurav Sanap, Snehal Shenoy, et al.
Drug Discovery Today (2020) Vol. 26, Iss. 1, pp. 80-93
Open Access | Times Cited: 1023
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery
Nicholas Ekow Thomford, Dimakatso Alice Senthebane, Arielle Rowe, et al.
International Journal of Molecular Sciences (2018) Vol. 19, Iss. 6, pp. 1578-1578
Open Access | Times Cited: 1005
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
Next-Generation Machine Learning for Biological Networks
Diogo M. Camacho, Katherine M. Collins, Rani K. Powers, et al.
Cell (2018) Vol. 173, Iss. 7, pp. 1581-1592
Open Access | Times Cited: 805
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Xin Yang, Yifei Wang, Ryan Byrne, et al.
Chemical Reviews (2019) Vol. 119, Iss. 18, pp. 10520-10594
Open Access | Times Cited: 749
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
Automating drug discovery
Gisbert Schneider
Nature Reviews Drug Discovery (2017) Vol. 17, Iss. 2, pp. 97-113
Open Access | Times Cited: 651
ADMET modeling approaches in drug discovery
Leonardo L. G. Ferreira, Adriano D. Andricopulo
Drug Discovery Today (2019) Vol. 24, Iss. 5, pp. 1157-1165
Closed Access | Times Cited: 566
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
Marta M. Stepniewska-Dziubinska, Piotr Zielenkiewicz, Paweł Siedlecki
Bioinformatics (2018) Vol. 34, Iss. 21, pp. 3666-3674
Open Access | Times Cited: 533
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: 470
Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review
Victor T. Sabe, Thandokuhle Ntombela, Lindiwe A. Jhamba, et al.
European Journal of Medicinal Chemistry (2021) Vol. 224, pp. 113705-113705
Closed Access | Times Cited: 436
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenhua Wu, Chang‐Yu Hsieh, et al.
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 399
A drug-likeness toolbox facilitates ADMET study in drug discovery
Chen‐Yang Jia, Jingyi Li, Ge‐Fei Hao, et al.
Drug Discovery Today (2019) Vol. 25, Iss. 1, pp. 248-258
Closed Access | Times Cited: 371
Big Data and Artificial Intelligence Modeling for Drug Discovery
Hao Zhu
The Annual Review of Pharmacology and Toxicology (2019) Vol. 60, Iss. 1, pp. 573-589
Open Access | Times Cited: 356
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare
Pandiaraj Manickam, Siva Ananth Mariappan, Sindhu Monica Murugesan, et al.
Biosensors (2022) Vol. 12, Iss. 8, pp. 562-562
Open Access | Times Cited: 349
Reinforced Adversarial Neural Computer for de Novo Molecular Design
Evgeny Putin, Arip Asadulaev, Yan A. Ivanenkov, et al.
Journal of Chemical Information and Modeling (2018) Vol. 58, Iss. 6, pp. 1194-1204
Open Access | Times Cited: 334
Artificial Intelligence in Drug Design
Gerhard Heßler, Karl‐Heinz Baringhaus
Molecules (2018) Vol. 23, Iss. 10, pp. 2520-2520
Open Access | Times Cited: 313
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: 302
Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges
Isabella Alvim Guedes, Felipe Siconha S. Pereira, Laurent E. Dardenne
Frontiers in Pharmacology (2018) Vol. 9
Open Access | Times Cited: 258
Potential of quantum computing for drug discovery
Yudong Cao, Jonathan Romero, Alán Aspuru‐Guzik
IBM Journal of Research and Development (2018) Vol. 62, Iss. 6, pp. 6:1-6:20
Closed Access | Times Cited: 251
Research and application of machine learning for additive manufacturing
Jian Qin, Fu Hu, Ying Liu, et al.
Additive manufacturing (2022) Vol. 52, pp. 102691-102691
Open Access | Times Cited: 244
The significance of artificial intelligence in drug delivery system design
Parichehr Hassanzadeh, Fatemeh Atyabi, Rassoul Dinarvand
Advanced Drug Delivery Reviews (2019) Vol. 151-152, pp. 169-190
Closed Access | Times Cited: 229
Integrated phenology and climate in rice yields prediction using machine learning methods
Yahui Guo, Yongshuo H. Fu, Fanghua Hao, et al.
Ecological Indicators (2020) Vol. 120, pp. 106935-106935
Open Access | Times Cited: 218
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Chao Shen, Junjie Ding, Zhe Wang, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2019) Vol. 10, Iss. 1
Closed Access | Times Cited: 217
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