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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:

Artificial Intelligence-Based Quantitative Structure–Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity
Natalia Czub, Jakub Szlęk, Adam Pacławski, et al.
Molecular Pharmaceutics (2023) Vol. 20, Iss. 5, pp. 2545-2555
Open Access | Times Cited: 15

Showing 15 citing articles:

Biologics, theranostics, and personalized medicine in drug delivery systems
Matteo Puccetti, Marilena Pariano, Aurélie Schoubben, et al.
Pharmacological Research (2024) Vol. 201, pp. 107086-107086
Open Access | Times Cited: 53
The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery
Hiroshi Komura, Reiko Watanabe, Kenji Mizuguchi
Pharmaceutics (2023) Vol. 15, Iss. 11, pp. 2619-2619
Open Access | Times Cited: 26
AI-Driven Decision-Making Applications in Pharmaceutical Sciences
Bancha Yingngam, Abhiruj Navabhatra, Polpan Sillapapibool
Advances in media, entertainment and the arts (AMEA) book series (2024), pp. 1-63
Closed Access | Times Cited: 5
SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery
Natalia Łapińska, Adam Pacławski, Jakub Szlęk, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 7, pp. 2150-2157
Open Access | Times Cited: 5
Application of artificial intelligence in drug design: A review
Simrandeep Singh, Navjot Kaur, Anita Gehlot
Computers in Biology and Medicine (2024) Vol. 179, pp. 108810-108810
Closed Access | Times Cited: 5
Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4R200L in Staphylococcus aureus
Shalini Mathpal, Tushar Joshi, P. Priyamvada, et al.
Molecular Diversity (2025)
Closed Access
Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning
D. Sharmistha, M Prabha, Ravi Kiran, et al.
SAR and QSAR in environmental research (2025), pp. 1-21
Closed Access
Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules
Davide Bassani, Neil Parrott, Nenad Manevski, et al.
Expert Opinion on Drug Discovery (2024) Vol. 19, Iss. 6, pp. 683-698
Open Access | Times Cited: 3
Computational screening of matrix metalloproteinase 3 inhibitors to counteract skin aging from phytochemicals of Nelumbo nucifera Gaertn
Amisha Bisht, Disha Tewari, Kalpana Rawat, et al.
Theoretical Chemistry Accounts (2024) Vol. 143, Iss. 6
Closed Access | Times Cited: 1
Combined data-driven and mechanism-based approaches for human-intestinal-absorption prediction in the early drug-discovery stage
Koichi Handa, Sakae Sugiyama, Michiharu Kageyama, et al.
Digital Discovery (2023) Vol. 2, Iss. 5, pp. 1577-1588
Open Access | Times Cited: 3
In Vitro Bioactivities and Shelf Life Protective Properties of Sumac Extracts Against Foodborne Pathogens and Computational Analysis of Sumac Compounds
Didem Berber, Toksoz Orcun, İpek Türkmenoğlu, et al.
(2024)
Closed Access
In vitro Bioactivities and Shelf Life Protective Properties of Sumac Extracts Against Foodborne Pathogens and Computational Analysis of Sumac Compounds
Orçun TOKSÖZ, İpek Türkmenoğlu, Didem Berber, et al.
Food Bioscience (2024) Vol. 61, pp. 104702-104702
Closed Access
Integrating (Deep) Machine Learning and Cheminformatics for Predicting Human Intestinal Absorption of Small Molecules
Orchid Baruah, Upashya Parasar, Anirban Borphukan, et al.
Computational Biology and Chemistry (2024) Vol. 113, pp. 108270-108270
Closed Access
QSAR and Machine Learning Predictors
Philipe Oliveira Fernandes, Vinícius Gonçalves Maltarollo
(2024), pp. 131-161
Closed Access
Combined Data-driven and Mechanism-based Approaches for Human-Intestinal-Absorption Prediction in Early Drug-Discovery Stage
Koichi Handa, Sakae Sugiyama, Michiharu Kageyama, et al.
(2023)
Open Access | Times Cited: 1
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