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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 Enables Accurate Prediction of Asparagine Deamidation Probability and Rate
Jared A. Delmar, Jihong Wang, Seo Woo Choi, et al.
Molecular Therapy — Methods & Clinical Development (2019) Vol. 15, pp. 264-274
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation
Harini Narayanan, Fabian Dingfelder, Alessandro Butté, et al.
Trends in Pharmacological Sciences (2021) Vol. 42, Iss. 3, pp. 151-165
Closed Access | Times Cited: 125
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: 25
Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins
Moritz Ertelt, Vikram Khipple Mulligan, Jack B. Maguire, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 3, pp. e1011939-e1011939
Open Access | Times Cited: 11
Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design
Daisuke Kuroda, Kouhei Tsumoto
Journal of Pharmaceutical Sciences (2020) Vol. 109, Iss. 5, pp. 1631-1651
Open Access | Times Cited: 50
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: 32
Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics
Weijie Zhang, Hao Wang, Nan Feng, et al.
Antibody Therapeutics (2022) Vol. 6, Iss. 1, pp. 13-29
Open Access | Times Cited: 30
The ice age – A review on formulation of Adeno-associated virus therapeutics
Philip Grossen, Irini Skaripa Koukelli, Joost van Haasteren, et al.
European Journal of Pharmaceutics and Biopharmaceutics (2023) Vol. 190, pp. 1-23
Open Access | Times Cited: 19
LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies
Tadeusz Satława, Mateusz Tarkowski, Sonia Wróbel, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 3, pp. e1011881-e1011881
Open Access | Times Cited: 6
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
Heat‐induced structural and chemical changes to a computationally designed miniprotein
Joshua Dudley, Sojeong Park, Oliver Cho, et al.
Protein Science (2024) Vol. 33, Iss. 6
Closed Access | Times Cited: 6
In silico prediction of post-translational modifications in therapeutic antibodies
Shabdita Vatsa
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 23
From cell line development to the formulated drug product: The art of manufacturing therapeutic monoclonal antibodies
Stefania Carrara, Michael Ulitzka, Julius Grzeschik, et al.
International Journal of Pharmaceutics (2020) Vol. 594, pp. 120164-120164
Closed Access | Times Cited: 38
Examining collagen preservation through glutamine deamidation at Denisova Cave
Samantha Brown, М. Б. Козликин, M. V. Shunkov, et al.
Journal of Archaeological Science (2021) Vol. 133, pp. 105454-105454
Closed Access | Times Cited: 27
iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features
Md Mofijul Islam, Sanjay Saha, Md Mahmudur Rahman, et al.
Proteins Structure Function and Bioinformatics (2018) Vol. 86, Iss. 7, pp. 777-789
Closed Access | Times Cited: 34
Deciphering deamidation and isomerization in therapeutic proteins: Effect of neighboring residue
Flaviyan Jerome Irudayanathan, Jonathan Zarzar, Jasper Lin, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 17
Multiplex Bioanalytical Methods for Comprehensive Characterization and Quantification of the Unique Complementarity-Determining-Region Deamidation of MEDI7247, an Anti-ASCT2 Pyrrolobenzodiazepine Antibody–Drug Conjugate
Yue Huang, Jiaqi Yuan, Ruipeng Mu, et al.
Antibodies (2023) Vol. 12, Iss. 4, pp. 66-66
Open Access | Times Cited: 10
Prediction Machines: Applied Machine Learning for Therapeutic Protein Design and Development
Tim J. Kamerzell, C. Russell Middaugh
Journal of Pharmaceutical Sciences (2020) Vol. 110, Iss. 2, pp. 665-681
Closed Access | Times Cited: 25
Antibody disulfide bond reduction and recovery during biopharmaceutical process development—A review
Tingwei Ren, Zhijun Tan, Vivekh Ehamparanathan, et al.
Biotechnology and Bioengineering (2021) Vol. 118, Iss. 8, pp. 2829-2844
Open Access | Times Cited: 22
High-throughput developability assays enable library-scale identification of producible protein scaffold variants
Alexander W. Golinski, Katelynn M. Mischler, Sidharth Laxminarayan, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 23
Open Access | Times Cited: 21
Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment
Christopher Negron, Joyce Fang, Michael J. McPherson, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 15
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
Machine learning prediction of methionine and tryptophan photooxidation susceptibility
Jared A. Delmar, Eugen Buehler, Ashwin K. Chetty, et al.
Molecular Therapy — Methods & Clinical Development (2021) Vol. 21, pp. 466-477
Open Access | Times Cited: 13
Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes
Clara Oliva, Ángela Arias, Pedro Ruiz‐Sala, et al.
Clinical Chemistry and Laboratory Medicine (CCLM) (2024) Vol. 62, Iss. 10, pp. 1991-2000
Closed Access | Times Cited: 1
Accurate prediction of antibody deamidations by combining high-throughput automated peptide mapping and protein language model-based deep learning
Ben Niu, Benjamin C. Lee, Lili Wang, et al.
(2024)
Open Access | Times Cited: 1
Molecular Modeling of the Deamidation Reaction in Solution: A Theoretical–Computational Study
Maria Laura De Sciscio, Alessandro Nicola Nardi, Fabio Centola, et al.
The Journal of Physical Chemistry B (2023) Vol. 127, Iss. 44, pp. 9550-9559
Open Access | Times Cited: 4
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