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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:

IgLM: Infilling language modeling for antibody sequence design
Richard W. Shuai, Jeffrey A. Ruffolo, Jeffrey J. Gray
Cell Systems (2023) Vol. 14, Iss. 11, pp. 979-989.e4
Open Access | Times Cited: 56

Showing 1-25 of 56 citing articles:

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery
Wiktoria Wilman, Sonia Wróbel, Weronika Bielska, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Open Access | Times Cited: 59
Unlockingde novoantibody design with generative artificial intelligence
Amir Shanehsazzadeh, Matt McPartlon, George W. Kasun, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 38
Toward real-world automated antibody design with combinatorial Bayesian optimization
Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit, et al.
Cell Reports Methods (2023) Vol. 3, Iss. 1, pp. 100374-100374
Open Access | Times Cited: 26
Adaptive immune receptor repertoire analysis
Vanessa Mhanna, Habib Bashour, Khang Lê Quý, et al.
Nature Reviews Methods Primers (2024) Vol. 4, Iss. 1
Closed Access | Times Cited: 12
Large language models for science and medicine
Amalio Telenti, Michael Auli, Brian Hie, et al.
European Journal of Clinical Investigation (2024) Vol. 54, Iss. 6
Closed Access | Times Cited: 12
De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model
Haohuai He, Bing He, Lei Guan, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 12
An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies
Yiquan Wang, Huibin Lv, Qi Wen Teo, et al.
Immunity (2024) Vol. 57, Iss. 10, pp. 2453-2465.e7
Open Access | Times Cited: 9
Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction
Meng Wang, Jonathan Patsenker, Henry Li, et al.
PLoS Computational Biology (2025) Vol. 21, Iss. 3, pp. e1012153-e1012153
Open Access | Times Cited: 1
AI models for protein design are driving antibody engineering
Michael Chungyoun, Jeffrey J. Gray
Current Opinion in Biomedical Engineering (2023) Vol. 28, pp. 100473-100473
Open Access | Times Cited: 18
The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design
Eugenio Gallo
Journal of Biomedical Science (2024) Vol. 31, Iss. 1
Open Access | Times Cited: 6
A new era of antibody discovery: an in-depth review of AI-driven approaches
Jin Cheng, Tianjian Liang, Xiang‐Qun Xie, et al.
Drug Discovery Today (2024) Vol. 29, Iss. 6, pp. 103984-103984
Closed Access | Times Cited: 6
For antibody sequence generative modeling, mixture models may be all you need
Jonathan Parkinson, Wei Wang
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 6
Adapting protein language models for structure-conditioned design
Jeffrey A. Ruffolo, Aadyot Bhatnagar, Joel Beazer, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 6
The promises of large language models for protein design and modeling
Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, et al.
Frontiers in Bioinformatics (2023) Vol. 3
Open Access | Times Cited: 13
NanoAbLLaMA: construction of nanobody libraries with protein large language models
Xin Wang, Haotian Chen, Bo Chen, et al.
Frontiers in Chemistry (2025) Vol. 13
Open Access
Interdisciplinary Approaches to Leverage Biomarker Discovery for Cancer Treatment
Fatemeh Khatami, Nima Rezaei
Interdisciplinary cancer research (2025)
Closed Access
Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions
Michael Mullin, James McClory, Winston Haynes, et al.
mAbs (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 4
Phage display technology and its impact in the discovery of novel protein-based drugs
Catherine J. Hutchings, Aaron K. Sato
Expert Opinion on Drug Discovery (2024) Vol. 19, Iss. 8, pp. 887-915
Open Access | Times Cited: 4
Antibody design using deep learning: from sequence and structure design to affinity maturation
Sara Joubbi, Alessio Micheli, Paolo Milazzo, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 4
Two decades of advances in sequence-based prediction of MoRFs, disorder-to-order transitioning binding regions
Jiangning Song, Lukasz Kurgan
Expert Review of Proteomics (2025)
Closed Access
Retrospective SARS-CoV-2 human antibody development trajectories are largely sparse and permissive
Monica B. Kirby, Brian M. Petersen, Jonathan G. Faris, et al.
Proceedings of the National Academy of Sciences (2025) Vol. 122, Iss. 4
Open Access
How well do contextual protein encodings learn structure, function, and evolutionary context?
Sai Pooja Mahajan, Fátima A. Dávila-Hernández, Jeffrey A. Ruffolo, et al.
Cell Systems (2025) Vol. 16, Iss. 3, pp. 101201-101201
Closed Access
Language models for protein design
Jin Seop Lee, Osama Abdin, Philip M. Kim
Current Opinion in Structural Biology (2025) Vol. 92, pp. 103027-103027
Closed Access
Application of Computer Vision and Image Analysis in New Product Market Testing
B. Ding
Smart innovation, systems and technologies (2025), pp. 341-350
Closed Access
Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools
Varun Dewaker, Vivek Kumar Morya, Yeon-Ju Kim, et al.
Biomarker Research (2025) Vol. 13, Iss. 1
Open Access
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