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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:

Machine learning applications in drug development
Clémence Réda, Emilie Kaufmann, Andrée Delahaye‐Duriez
Computational and Structural Biotechnology Journal (2019) Vol. 18, pp. 241-252
Open Access | Times Cited: 198

Showing 1-25 of 198 citing articles:

Addressing bias in big data and AI for health care: A call for open science
Natalia Norori, Qiyang Hu, Florence M. Aellen, et al.
Patterns (2021) Vol. 2, Iss. 10, pp. 100347-100347
Open Access | Times Cited: 400
Machine Learning Methods in Drug Discovery
Lauv Patel, Tripti Shukla, Xiuzhen Huang, et al.
Molecules (2020) Vol. 25, Iss. 22, pp. 5277-5277
Open Access | Times Cited: 315
Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R. Jamasb, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 185
Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine
Ryuji Hamamoto, Kruthi Suvarna, Masayoshi Yamada, et al.
Cancers (2020) Vol. 12, Iss. 12, pp. 3532-3532
Open Access | Times Cited: 155
The use of machine learning in rare diseases: a scoping review
Julia Schaefer, Moritz Lehne, Josef Schepers, et al.
Orphanet Journal of Rare Diseases (2020) Vol. 15, Iss. 1
Open Access | Times Cited: 149
Advanced machine-learning techniques in drug discovery
Moe Elbadawi, Simon Gaisford, Abdul W. Basit
Drug Discovery Today (2020) Vol. 26, Iss. 3, pp. 769-777
Open Access | Times Cited: 138
Generative artificial intelligence empowers digital twins in drug discovery and clinical trials
Maria Bordukova, Nikita Makarov, Raul Rodriguez‐Esteban, et al.
Expert Opinion on Drug Discovery (2023) Vol. 19, Iss. 1, pp. 33-42
Open Access | Times Cited: 65
Advances in artificial intelligence for drug delivery and development: A comprehensive review
Amol D. Gholap, Md Jasim Uddin, Md. Faiyazuddin, et al.
Computers in Biology and Medicine (2024) Vol. 178, pp. 108702-108702
Closed Access | Times Cited: 37
Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine
Dolores R. Serrano, Francis C. Luciano, Brayan J. Anaya, et al.
Pharmaceutics (2024) Vol. 16, Iss. 10, pp. 1328-1328
Open Access | Times Cited: 37
MACHINE LEARNING IN DRUG DISCOVERY: A CRITICAL REVIEW OF APPLICATIONS AND CHALLENGES
Francisca Chibugo Udegbe, Ogochukwu Roseline Ebulue, Charles Chukwudalu Ebulue, et al.
Computer Science & IT Research Journal (2024) Vol. 5, Iss. 4, pp. 892-902
Open Access | Times Cited: 36
Machine learning for micro- and nanorobots
Lidong Yang, Jialin Jiang, Fengtong Ji, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 6, pp. 605-618
Closed Access | Times Cited: 16
Rapid and scalable personalized ASO screening in patient-derived organoids
John C. Means, Anabel Martinez‐Bengochea, Daniel Louiselle, et al.
Nature (2025) Vol. 638, Iss. 8049, pp. 237-243
Open Access | Times Cited: 3
Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review
Hafsa Bareen Syeda, Mahanazuddin Syed, Kevin W. Sexton, et al.
JMIR Medical Informatics (2020) Vol. 9, Iss. 1, pp. e23811-e23811
Open Access | Times Cited: 137
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: 95
Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges
Junjie Peng, Elizabeth C. Jury, Pierre Dönnes, et al.
Frontiers in Pharmacology (2021) Vol. 12
Open Access | Times Cited: 92
Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
Maciej Staszak, Katarzyna Staszak, Karolina Wieszczycka, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 12, Iss. 2
Open Access | Times Cited: 89
Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
Agam Bansal, Rana Prathap Padappayil, Chandan Garg, et al.
Journal of Medical Systems (2020) Vol. 44, Iss. 9
Open Access | Times Cited: 86
Skin lesion segmentation using fully convolutional networks: A comparative experimental study
Ruya Kaymak, Çağrı Kaymak, Ayşegül Uçar
Expert Systems with Applications (2020) Vol. 161, pp. 113742-113742
Closed Access | Times Cited: 71
Use of artificial intelligence to enhance phenotypic drug discovery
Sophia Malandraki-Miller, Paul R. Riley
Drug Discovery Today (2021) Vol. 26, Iss. 4, pp. 887-901
Closed Access | Times Cited: 57
Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?
Alexandre de Fátima Cobre, Dile Pontarolo Stremel, Guilhermina Rodrigues Noleto, et al.
Computers in Biology and Medicine (2021) Vol. 134, pp. 104531-104531
Open Access | Times Cited: 57
Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin
Lina Keutzer, huifang you, Ali Farnoud, et al.
Pharmaceutics (2022) Vol. 14, Iss. 8, pp. 1530-1530
Open Access | Times Cited: 52
Machine Learning guided early drug discovery of small molecules
Nikhil Pillai, Aparajita Dasgupta, Sirimas Sudsakorn, et al.
Drug Discovery Today (2022) Vol. 27, Iss. 8, pp. 2209-2215
Open Access | Times Cited: 47
Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing—a Review
Brigitta Nagy, Dorián László Galata, Attila Farkas, et al.
The AAPS Journal (2022) Vol. 24, Iss. 4
Open Access | Times Cited: 46
Comparative analysis between 2D and 3D colorectal cancer culture models for insights into cellular morphological and transcriptomic variations
Zaid Nsaif Abbas, Ali Z. Al‐Saffar, Saba Mahdi Jasim, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 33
Machine learning applications for COVID-19: a state-of-the-art review
Firuz Kamalov, Aswani Kumar Cherukuri, Hana Sulieman, et al.
Elsevier eBooks (2023), pp. 277-289
Open Access | Times Cited: 31
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