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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 designs non-hemolytic antimicrobial peptides
Alice Capecchi, Xingguang Cai, Hippolyte Personne, et al.
Chemical Science (2021) Vol. 12, Iss. 26, pp. 9221-9232
Open Access | Times Cited: 103

Showing 1-25 of 103 citing articles:

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP
Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 67
Advancements, challenges and future perspectives on peptide-based drugs: Focus on antimicrobial peptides
Xiaofang Luo, Huifang Chen, Yannan Song, et al.
European Journal of Pharmaceutical Sciences (2022) Vol. 181, pp. 106363-106363
Closed Access | Times Cited: 64
Molecular Glue Discovery: Current and Future Approaches
Jeffrey A. Dewey, Clémence Delalande, Saara‐Anne Azizi, et al.
Journal of Medicinal Chemistry (2023) Vol. 66, Iss. 14, pp. 9278-9296
Open Access | Times Cited: 39
Machine learning for antimicrobial peptide identification and design
Fangping Wan, Felix Wong, James J. Collins, et al.
Nature Reviews Bioengineering (2024) Vol. 2, Iss. 5, pp. 392-407
Closed Access | Times Cited: 36
Deep-learning-enabled antibiotic discovery through molecular de-extinction
Fangping Wan, Marcelo D. T. Torres, Jacqueline Peng, et al.
Nature Biomedical Engineering (2024) Vol. 8, Iss. 7, pp. 854-871
Open Access | Times Cited: 36
Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization
Lizhi Yue, Liya Song, Siyu Zhu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 14
Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens
Beilun Wang, Peijun Lin, Yi Zhong, et al.
Nature Microbiology (2025)
Closed Access | Times Cited: 4
Deep generative models for peptide design
Fangping Wan, Daphne Kontogiorgos-Heintz, César de la Fuente‐Núñez
Digital Discovery (2022) Vol. 1, Iss. 3, pp. 195-208
Open Access | Times Cited: 63
Research progress in the screening and evaluation of umami peptides
Lulu Qi, Xinchang Gao, Daodong Pan, et al.
Comprehensive Reviews in Food Science and Food Safety (2022) Vol. 21, Iss. 2, pp. 1462-1490
Closed Access | Times Cited: 57
Machine Learning Advances in Microbiology: A Review of Methods and Applications
Yiru Jiang, Jing Luo, Danqing Huang, et al.
Frontiers in Microbiology (2022) Vol. 13
Open Access | Times Cited: 40
Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity
Alberto A. Robles-Loaiza, Edgar A. Pinos-Tamayo, Bruno Mendes, et al.
Pharmaceuticals (2022) Vol. 15, Iss. 3, pp. 323-323
Open Access | Times Cited: 39
Recent advances in the development of therapeutic peptides
John Fetse, Sashi Kandel, Umar‐Farouk Mamani, et al.
Trends in Pharmacological Sciences (2023) Vol. 44, Iss. 7, pp. 425-441
Open Access | Times Cited: 29
PeptideBERT: A Language Model Based on Transformers for Peptide Property Prediction
Chakradhar Guntuboina, Adrita Das, Parisa Mollaei, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 46, pp. 10427-10434
Open Access | Times Cited: 25
Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology
Alexa Sowers, Guangshun Wang, Malcolm Xing, et al.
Microorganisms (2023) Vol. 11, Iss. 5, pp. 1129-1129
Open Access | Times Cited: 23
Serverless Prediction of Peptide Properties with Recurrent Neural Networks
Mehrad Ansari, Andrew Dickson White
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 8, pp. 2546-2553
Open Access | Times Cited: 21
Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design
Xinyi Wu, Huitian Lin, Renren Bai, et al.
European Journal of Medicinal Chemistry (2024) Vol. 268, pp. 116262-116262
Closed Access | Times Cited: 8
De novo antioxidant peptide design via machine learning and DFT studies
Parsa Hesamzadeh, Abdolvahab Seif, Kazem Mahmoudzadeh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 8
Specifically targeted antimicrobial peptides synergize with bacterial-entrapping peptide against systemic MRSA infections
Bocheng Xu, Lin Wang, Chen Yang, et al.
Journal of Advanced Research (2024)
Open Access | Times Cited: 7
Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning
Qichang Dong, Shaohua Wang, Ying Miao, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications
Suchita Paul, Sandeep Verma, Yu‐Chie Chen
ACS Infectious Diseases (2024) Vol. 10, Iss. 4, pp. 1034-1055
Open Access | Times Cited: 7
From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review
José Manuel Pérez de la Lastra, Samuel J. T. Wardell, Tarun Pal, et al.
Journal of Medical Systems (2024) Vol. 48, Iss. 1
Open Access | Times Cited: 7
AI Methods for Antimicrobial Peptides: Progress and Challenges
Carlos A. Brizuela, Gary Liu, J Stokes, et al.
Microbial Biotechnology (2025) Vol. 18, Iss. 1
Open Access
Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION
Bahareh Hasan Pour
Mycoses (2025) Vol. 68, Iss. 1
Closed Access
Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance
Angela Cesaro, Samuel C. Hoffman, Payel Das, et al.
npj Antimicrobials and Resistance (2025) Vol. 3, Iss. 1
Open Access
The Role of Artificial Intelligence and Machine Learning in Predicting and Combating Antimicrobial Resistance
Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan, et al.
Computational and Structural Biotechnology Journal (2025) Vol. 27, pp. 423-439
Open Access
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