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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.

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Showing 1-25 of 53 citing articles:

Computational and artificial intelligence-based methods for antibody development
Ji‐Sun Kim, Matthew McFee, Qiao Fang, et al.
Trends in Pharmacological Sciences (2023) Vol. 44, Iss. 3, pp. 175-189
Open Access | Times Cited: 84
Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities
Thanh Tung Khuat, Robert Bassett, Ellen Otte, et al.
Computers & Chemical Engineering (2024) Vol. 182, pp. 108585-108585
Open Access | Times Cited: 19
Blueprint for antibody biologics developability
Carl Mieczkowski, Xuejin Zhang, Dana Lee, et al.
mAbs (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 28
High concentration formulation developability approaches and considerations
Jonathan Zarzar, Tarik A. Khan, Maniraj Bhagawati, et al.
mAbs (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 27
Innovations and trends in antibody repertoire analysis
Douglas R. Townsend, Dalton M. Towers, Jason J. Lavinder, et al.
Current Opinion in Biotechnology (2024) Vol. 86, pp. 103082-103082
Closed Access | Times Cited: 7
Reduction of monoclonal antibody viscosity using interpretable machine learning
Emily K. Makowski, Hsin-Ting Chen, Tiexin Wang, et al.
mAbs (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 7
DOTAD: A Database of Therapeutic Antibody Developability
Wenzhen Li, Hong‐Yan Lin, Ziru Huang, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 3, pp. 623-634
Closed Access | Times Cited: 6
Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals
Michaela Cohrs, Shiwoo Koak, Yejin Lee, et al.
Lecture notes in computer science (2025), pp. 86-96
Open Access
Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals
Amita Puranik, Prajakta Dandekar, Ratnesh Jain
Biotechnology Progress (2022) Vol. 38, Iss. 6
Closed Access | Times Cited: 29
Accelerating therapeutic protein design with computational approaches toward the clinical stage
Zhidong Chen, Xinpei Wang, Xu Chen, et al.
Computational and Structural Biotechnology Journal (2023) Vol. 21, pp. 2909-2926
Open Access | Times Cited: 17
Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning
Emily K. Makowski, Tiexin Wang, Jennifer M. Zupancic, et al.
Nature Biomedical Engineering (2023) Vol. 8, Iss. 1, pp. 45-56
Closed Access | Times Cited: 17
Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects
Ganggang Bai, Chuance Sun, Ziang Guo, et al.
Seminars in Cancer Biology (2023) Vol. 95, pp. 13-24
Closed Access | Times Cited: 14
Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling
Eliott Park, Saeed Izadi
mAbs (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 5
A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles
Georgina Bethany Armstrong, Vidhi Shah, Paula Sanches, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 2345-2357
Open Access | Times Cited: 4
DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity
Pin‐Kuang Lai
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 2143-2152
Open Access | Times Cited: 22
How can we discover developable antibody-based biotherapeutics?
Joschka Bauer, Nandhini Rajagopal, Priyanka Gupta, et al.
Frontiers in Molecular Biosciences (2023) Vol. 10
Open Access | Times Cited: 14
Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies
Marissa Mock, Alex W. Jacobitz, Christopher J. Langmead, et al.
mAbs (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 13
Biophysical Determinants for the Viscosity of Concentrated Monoclonal Antibody Solutions
Ilaria Mosca, Kévin Pounot, Christian Beck, et al.
Molecular Pharmaceutics (2023) Vol. 20, Iss. 9, pp. 4698-4713
Closed Access | Times Cited: 11
DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability
Lateefat Kalejaye, I-En Wu, T. Philip Terry, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 2220-2229
Open Access | Times Cited: 3
Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics
Kuin Tian Pang, Yuansheng Yang, Wei Zhang, et al.
Biotechnology Advances (2023) Vol. 67, pp. 108192-108192
Open Access | Times Cited: 10
Recent advances in generative biology for biotherapeutic discovery
Marissa Mock, Christopher J. Langmead, Peter Grandsard, et al.
Trends in Pharmacological Sciences (2024) Vol. 45, Iss. 3, pp. 255-267
Open Access | Times Cited: 3
Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks
Jonathan Schmitt, Abbas Razvi, Christoph Grapentin
mAbs (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Characterization of antibody surface properties and selection of a diverse subset for development of a generic size-exclusion chromatography method
Marika Ejby Reinau, Dennis Åsberg
Journal of Chromatography A (2024) Vol. 1716, pp. 464652-464652
Closed Access | Times Cited: 2
Stability of Protein Pharmaceuticals: Recent Advances
Mark C. Manning, Ryan E. Holcomb, R. W. Payne, et al.
Pharmaceutical Research (2024) Vol. 41, Iss. 7, pp. 1301-1367
Closed Access | Times Cited: 2
A First Insight into the Developability of an Immunoglobulin G3: A Combined Computational and Experimental Approach
Georgina Bethany Armstrong, Alan T. Lewis, Vidhi Shah, et al.
ACS Pharmacology & Translational Science (2024) Vol. 7, Iss. 8, pp. 2439-2451
Open Access | Times Cited: 2
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