Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 in pharmacometrics: Opportunities and challenges
Mason McComb, Robert R. Bies, Murali Ramanathan
British Journal of Clinical Pharmacology (2021) Vol. 88, Iss. 4, pp. 1482-1499
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare
Lara Marques, Bárbara Costa, Mariana Pereira, et al.
Pharmaceutics (2024) Vol. 16, Iss. 3, pp. 332-332
Open Access | Times Cited: 85
AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Michael Schauperl, R. Aldrin Denny
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 13, pp. 3142-3156
Closed Access | Times Cited: 80
Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing
Ethan A. Poweleit, Alexander A. Vinks, Tomoyuki Mizuno
Therapeutic Drug Monitoring (2023) Vol. 45, Iss. 2, pp. 143-150
Open Access | Times Cited: 50
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
Applications and prospects of cryo-EM in drug discovery
Kongfu Zhu, Chuang Yuan, Yongming Du, et al.
Military Medical Research (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 35
Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices
Nadia Terranova, Didier Renard, Mohamed H. Shahin, et al.
Clinical Pharmacology & Therapeutics (2023) Vol. 115, Iss. 4, pp. 658-672
Open Access | Times Cited: 30
Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development
Wei Gao, Jiang Liu, Blerta Shtylla, et al.
CPT Pharmacometrics & Systems Pharmacology (2023) Vol. 13, Iss. 5, pp. 691-709
Open Access | Times Cited: 23
The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review
Phuvamin Suriyaamporn, Boonnada Pamornpathomkul, Prasopchai Patrojanasophon, et al.
AAPS PharmSciTech (2024) Vol. 25, Iss. 6
Closed Access | Times Cited: 12
Model‐Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing
Elena M. Tosca, Alessandro De Carlo, Davide Ronchi, et al.
Clinical Pharmacology & Therapeutics (2024) Vol. 116, Iss. 3, pp. 619-636
Open Access | Times Cited: 9
Success stories of AI in drug discovery - where do things stand?
Kit‐Kay Mak, Madhu Katyayani Balijepalli, Mallikarjuna Rao Pichika
Expert Opinion on Drug Discovery (2021) Vol. 17, Iss. 1, pp. 79-92
Closed Access | Times Cited: 43
Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations
Alexander Janßen, Frank C. Bennis, Ron A. A. Mathôt
Pharmaceutics (2022) Vol. 14, Iss. 9, pp. 1814-1814
Open Access | Times Cited: 32
Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates
Bo‐Hao Tang, Jinyuan Zhang, Karel Allegaert, et al.
Clinical Pharmacokinetics (2023) Vol. 62, Iss. 8, pp. 1105-1116
Closed Access | Times Cited: 21
Warfarin–A natural anticoagulant: A review of research trends for precision medication
Ling Xue, Rajeev K. Singla, Shan He, et al.
Phytomedicine (2024) Vol. 128, pp. 155479-155479
Closed Access | Times Cited: 7
A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors
Jasmine H. Hughes, Ron J. Keizer
CPT Pharmacometrics & Systems Pharmacology (2021) Vol. 10, Iss. 10, pp. 1150-1160
Open Access | Times Cited: 38
Introduction of an artificial neural network–based method for concentration‐time predictions
Dominic Stefan Bräm, Neil Parrott, Lucy Hutchinson, et al.
CPT Pharmacometrics & Systems Pharmacology (2022) Vol. 11, Iss. 6, pp. 745-754
Open Access | Times Cited: 23
Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence
Negin Mozafari, Niloofar Mozafari, Ali Dehshahri, et al.
Molecular Pharmaceutics (2023) Vol. 20, Iss. 8, pp. 3757-3778
Closed Access | Times Cited: 14
Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians
Kevin Meesters, Violeta Balbas‐Martinez, Karel Allegaert, et al.
Pediatric Drugs (2024) Vol. 26, Iss. 4, pp. 365-379
Closed Access | Times Cited: 5
Machine learning applications in healthcare sector: An overview
Virendra Kumar Verma, Savita Verma
Materials Today Proceedings (2021) Vol. 57, pp. 2144-2147
Closed Access | Times Cited: 32
Low-dimensional neural ODEs and their application in pharmacokinetics
Dominic Stefan Bräm, Uri Nahum, Johannes Schropp, et al.
Journal of Pharmacokinetics and Pharmacodynamics (2023) Vol. 51, Iss. 2, pp. 123-140
Open Access | Times Cited: 11
Reinforcement Learning and PK‐PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma
Alessandro De Carlo, Elena M. Tosca, Martina Fantozzi, et al.
Clinical Pharmacology & Therapeutics (2024) Vol. 115, Iss. 4, pp. 825-838
Closed Access | Times Cited: 4
An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine
Xiuqing Zhu, Jinqing Hu, Tao Xiao, et al.
Frontiers in Pharmacology (2022) Vol. 13
Open Access | Times Cited: 17
Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine
Richard Khusial, Robert R. Bies, Ayman Akil
Pharmaceutics (2023) Vol. 15, Iss. 4, pp. 1139-1139
Open Access | Times Cited: 10
Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?
Marcus J. Tindall, L. Cucurull-Sanchez, Hitesh Mistry, et al.
Journal of Pharmacology and Experimental Therapeutics (2023) Vol. 387, Iss. 1, pp. 92-99
Open Access | Times Cited: 10
Machine Learning for Prediction of Drug Concentrations: Application and Challenges
Shuqi Huang, Qihan Xu, Guoping Yang, et al.
Clinical Pharmacology & Therapeutics (2025)
Closed Access
Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations
Diego Valderrama, Olga Teplytska, Luca Marie Koltermann, et al.
CPT Pharmacometrics & Systems Pharmacology (2025)
Open Access
Page 1 - Next Page

Scroll to top