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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:

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: 721

Showing 1-25 of 721 citing articles:

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: 966
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: 812
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: 626
The role of artificial intelligence in healthcare: a structured literature review
Silvana Secinaro, Davide Calandra, Aurelio Secinaro, et al.
BMC Medical Informatics and Decision Making (2021) Vol. 21, Iss. 1
Open Access | Times Cited: 569
Computational approaches streamlining drug discovery
Anastasiia Sadybekov, Vsevolod Katritch
Nature (2023) Vol. 616, Iss. 7958, pp. 673-685
Open Access | Times Cited: 470
Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
Hui Wen Loh, Chui Ping Ooi, Silvia Seoni, et al.
Computer Methods and Programs in Biomedicine (2022) Vol. 226, pp. 107161-107161
Open Access | Times Cited: 395
Machine Learning for Electronically Excited States of Molecules
Julia Westermayr, Philipp Marquetand
Chemical Reviews (2020) Vol. 121, Iss. 16, pp. 9873-9926
Open Access | Times Cited: 326
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
Transfer Learning for Drug Discovery
Chenjing Cai, Shiwei Wang, Youjun Xu, et al.
Journal of Medicinal Chemistry (2020) Vol. 63, Iss. 16, pp. 8683-8694
Closed Access | Times Cited: 269
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Sezen Vatansever, Avner Schlessinger, Daniel Wacker, et al.
Medicinal Research Reviews (2020) Vol. 41, Iss. 3, pp. 1427-1473
Open Access | Times Cited: 242
Discovering Anti-Cancer Drugs via Computational Methods
Wenqiang Cui, Adnane Aouidate, Shouguo Wang, et al.
Frontiers in Pharmacology (2020) Vol. 11
Open Access | Times Cited: 240
m6A modification: recent advances, anticancer targeted drug discovery and beyond
Lijuan Deng, Wei-Qing Deng, Shu-Ran Fan, et al.
Molecular Cancer (2022) Vol. 21, Iss. 1
Open Access | Times Cited: 240
Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
Varnavas D. Mouchlis, Antreas Afantitis, Angela Serra, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 4, pp. 1676-1676
Open Access | Times Cited: 229
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm
Qifeng Bai, Shuoyan Tan, Tingyang Xu, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 216
In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery
Lauro Ribeiro de Souza Neto, José Teófilo Moreira-Filho, Bruno J. Neves, et al.
Frontiers in Chemistry (2020) Vol. 8
Open Access | Times Cited: 194
Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow
Chenglong Xie, Xu‐Xu Zhuang, Zhangming Niu, et al.
Nature Biomedical Engineering (2022) Vol. 6, Iss. 1, pp. 76-93
Open Access | Times Cited: 193
Targeting ferroptosis opens new avenues for the development of novel therapeutics
Shumin Sun, Jie Shen, Jianwei Jiang, et al.
Signal Transduction and Targeted Therapy (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 179
Generative molecular design in low data regimes
Michaël Moret, Lukas Friedrich, Francesca Grisoni, et al.
Nature Machine Intelligence (2020) Vol. 2, Iss. 3, pp. 171-180
Closed Access | Times Cited: 173
Multifunctional hydrogel as wound dressing for intelligent wound monitoring
Lirong Wang, Mengyun Zhou, Tailin Xu, et al.
Chemical Engineering Journal (2022) Vol. 433, pp. 134625-134625
Closed Access | Times Cited: 173
A critical overview of computational approaches employed for COVID-19 drug discovery
Eugene Muratov, Rommie E. Amaro, Carolina Horta Andrade, et al.
Chemical Society Reviews (2021) Vol. 50, Iss. 16, pp. 9121-9151
Open Access | Times Cited: 171
Recent advances in laser-induced breakdown spectroscopy quantification: From fundamental understanding to data processing
Zhe Wang, Muhammad Sher Afgan, Weilun Gu, et al.
TrAC Trends in Analytical Chemistry (2021) Vol. 143, pp. 116385-116385
Closed Access | Times Cited: 166
LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening
Viet‐Khoa Tran‐Nguyen, Célien Jacquemard, Didier Rognan
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 9, pp. 4263-4273
Open Access | Times Cited: 162
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning
Onat Kadioglu, Mohamed E.M. Saeed, Henry Johannes Greten, et al.
Computers in Biology and Medicine (2021) Vol. 133, pp. 104359-104359
Open Access | Times Cited: 160
Perspective on integrating machine learning into computational chemistry and materials science
Julia Westermayr, Michael Gastegger, Kristof T. Schütt, et al.
The Journal of Chemical Physics (2021) Vol. 154, Iss. 23
Open Access | Times Cited: 157
InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein–Ligand Interaction Predictions
Dejun Jiang, Chang‐Yu Hsieh, Zhenhua Wu, et al.
Journal of Medicinal Chemistry (2021) Vol. 64, Iss. 24, pp. 18209-18232
Closed Access | Times Cited: 152
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