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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:

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning
Dejun Jiang, Tailong Lei, Zhe Wang, et al.
Journal of Cheminformatics (2020) Vol. 12, Iss. 1
Open Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

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: 405
Computational Approaches in Preclinical Studies on Drug Discovery and Development
Fengxu Wu, Yuquan Zhou, Langhui Li, et al.
Frontiers in Chemistry (2020) Vol. 8
Open Access | Times Cited: 267
Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
Manish Tripathi, Abhigyan Nath, T.P. Singh, et al.
Molecular Diversity (2021) Vol. 25, Iss. 3, pp. 1439-1460
Open Access | Times Cited: 97
A Critical Review on Transplacental Transfer of Per- and Polyfluoroalkyl Substances: Prenatal Exposure Levels, Characteristics, and Mechanisms
Donghui Ma, Yao Lü, Yong Liang, et al.
Environmental Science & Technology (2021) Vol. 56, Iss. 10, pp. 6014-6026
Closed Access | Times Cited: 95
FP-ADMET: a compendium of fingerprint-based ADMET prediction models
Vishwesh Venkatraman
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 82
Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models
Dejun Jiang, Zhenxing Wu, Chang‐Yu Hsieh, et al.
Research Square (Research Square) (2020)
Open Access | Times Cited: 58
3D‐QSAR, molecular docking, molecular dynamics, and ADME/T analysis of marketed and newly designed flavonoids as inhibitors of Bcl‐2 family proteins for targeting U‐87 glioblastoma
Alireza Poustforoosh, Sanaz Faramarz, Mohammad Hadi Nematollahi, et al.
Journal of Cellular Biochemistry (2021) Vol. 123, Iss. 2, pp. 390-405
Closed Access | Times Cited: 46
Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning
Liangxu Xie, Lei Xu, Ren Kong, et al.
Frontiers in Pharmacology (2020) Vol. 11
Open Access | Times Cited: 50
Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics
Alya A. Arabi
Future Drug Discovery (2021) Vol. 3, Iss. 2
Open Access | Times Cited: 37
BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees
José Teófilo Moreira-Filho, Rodolpho C. Braga, Jade Milhomem Lemos, et al.
Artificial Intelligence in the Life Sciences (2021) Vol. 1, pp. 100013-100013
Open Access | Times Cited: 37
A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence
Sanjeevi Pandiyan, Li Wang
Computers in Biology and Medicine (2022) Vol. 150, pp. 106140-106140
Closed Access | Times Cited: 26
In silico drug discovery: a machine learning-driven systematic review
Sema Atasever
Medicinal Chemistry Research (2024)
Closed Access | Times Cited: 5
C@PA: Computer-Aided Pattern Analysis to Predict Multitarget ABC Transporter Inhibitors
Vigneshwaran Namasivayam, Katja Silbermann, Michael Wiese, et al.
Journal of Medicinal Chemistry (2021) Vol. 64, Iss. 6, pp. 3350-3366
Open Access | Times Cited: 30
In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods
Longqiang Li, Lu Zhou, Guixia Liu, et al.
Chemical Research in Toxicology (2022) Vol. 35, Iss. 9, pp. 1614-1624
Closed Access | Times Cited: 21
Machine Learning Techniques Applied to the Study of Drug Transporters
Xiaorui Kong, Kexin Lin, Gaolei Wu, et al.
Molecules (2023) Vol. 28, Iss. 16, pp. 5936-5936
Open Access | Times Cited: 12
Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence
Teng-Zhi Long, Dejun Jiang, Shaohua Shi, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3222-3236
Closed Access | Times Cited: 4
Artificial intelligence methods available for cancer research
Ankita Murmu, Balázs Győrffy
Frontiers of Medicine (2024) Vol. 18, Iss. 5, pp. 778-797
Open Access | Times Cited: 4
Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review
Anisie Uwimana, Giorgio Gnecco, Massimo Riccaboni
Computers in Biology and Medicine (2024) Vol. 184, pp. 109391-109391
Open Access | Times Cited: 4
Machine Learning Models Identify New Inhibitors for Human OATP1B1
Thomas R. Lane, Fabio Urbina, Xiaohong Zhang, et al.
Molecular Pharmaceutics (2022) Vol. 19, Iss. 11, pp. 4320-4332
Open Access | Times Cited: 18
Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification
Jiaju Wu, Linggang Kong, Ming Yi, et al.
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-14
Open Access | Times Cited: 17
MONSTROUS: a web-based chemical-transporter interaction profiler
Mohamed Diwan M. AbdulHameed, Souvik Dey, Zhen Xu, et al.
Frontiers in Pharmacology (2025) Vol. 16
Open Access
Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization
Seyed Saeed Masoomkhah, Khosro Rezaee, Mojtaba Ansari, et al.
Research Square (Research Square) (2025)
Closed Access
Assessment of Anti-Cancer Activity, DFT, Molecular Docking, Thermodynamic properties and ADMET studies of novel steroidal derivatives containing aceclofenac moiety
Anmol Verma, S. K. SHUKLA, Pratishtha Gupta, et al.
Journal of Molecular Structure (2025), pp. 142530-142530
Closed Access
Anti-cancer activity elucidation of geissolosimine as an MDM2-p53 interaction inhibitor: An in-silico study
Md Al-Amin, Rehnuma Tanjin, Manjurul Karim, et al.
PLoS ONE (2025) Vol. 20, Iss. 5, pp. e0323003-e0323003
Open Access
In silico analysis of soybean-derived umami peptides: Discovery and interaction mechanisms with T1R1/T1R3 receptor
Xiaoli Shen, Hao Zhang, Pengyin Zhang, et al.
Food Chemistry X (2025), pp. 102544-102544
Open Access
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