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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:

AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning
Milad Salem, Arash Keshavarzi Arshadi, J.S. Yuan
BMC Bioinformatics (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
Sijie Chen, Tong Lin, Ruchira Basu, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 82
Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning
Jielu Yan, Jianxiu Cai, Bob Zhang, et al.
Antibiotics (2022) Vol. 11, Iss. 10, pp. 1451-1451
Open Access | Times Cited: 71
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: 52
ToxinPred 3.0: An improved method for predicting the toxicity of peptides
Anand Singh Rathore, Shubham Choudhury, Akanksha Arora, et al.
Computers in Biology and Medicine (2024) Vol. 179, pp. 108926-108926
Open Access | Times Cited: 40
The role and future prospects of artificial intelligence algorithms in peptide drug development
Zhiheng Chen, Ruoxi Wang, Junqi Guo, et al.
Biomedicine & Pharmacotherapy (2024) Vol. 175, pp. 116709-116709
Open Access | Times Cited: 15
Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods
Hamid Teimouri, Angela Medvedeva, Anatoly B. Kolomeisky
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 6, pp. 1723-1733
Closed Access | Times Cited: 21
Artificial intelligence-driven antimicrobial peptide discovery
Paulina Szymczak, Ewa Szczurek
Current Opinion in Structural Biology (2023) Vol. 83, pp. 102733-102733
Open Access | Times Cited: 18
Antimicrobial peptides as drugs with double response against Mycobacterium tuberculosis coinfections in lung cancer
Giulia Polinário, Laura Maria Duran Gleriani Primo, Maiara Alane Baraldi Cerquetani Rosa, et al.
Frontiers in Microbiology (2023) Vol. 14
Open Access | Times Cited: 12
Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
Fernando Lobo, Maily Selena González, Alicia Boto, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 12, pp. 10270-10270
Open Access | Times Cited: 12
Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence
Kevin Castillo-Mendieta, Guillermín Agüero‐Chapín, Edgar Márquez, et al.
Chemical Research in Toxicology (2024) Vol. 37, Iss. 4, pp. 580-589
Closed Access | Times Cited: 4
TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning
Ke Yan, Hongwu Lv, Jiangyi Shao, et al.
Science China Information Sciences (2024) Vol. 67, Iss. 11
Closed Access | Times Cited: 4
Explainable artificial intelligence evolves antimicrobial peptides
Jeremie Alexander, Gary Liu, Jonathan Stokes
Nature Microbiology (2025)
Closed Access
Deep Learning for Antimicrobial Peptides: Computational Models and Databases
Xiangrun Zhou, Guixia Liu, Shuyuan Cao, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Advances of deep Neural Networks (DNNs) in the development of peptide drugs
Yuzhen Niu, Pingyang Qin, Ping Lin
Future Medicinal Chemistry (2025), pp. 1-15
Closed Access
Unlocking the Potential of Antimicrobial Peptides: Cutting-Edge Advances and Therapeutic Potential in Combating Bacterial Keratitis
Bingjia Xiao, Jie Wang, Jie Xing, et al.
Bioconjugate Chemistry (2025)
Closed Access
Leveraging protein language models for robust antimicrobial peptide detection
Lichao Zhang, Shuwen Xiong, Lei Xu, et al.
Methods (2025)
Closed Access
HMAMP: Designing Highly Potent Antimicrobial Peptides Using a Hypervolume-Driven Multiobjective Deep Generative Model
Wang Li, Yiping Liu, Xiangxiang Zeng, et al.
Journal of Medicinal Chemistry (2025)
Closed Access
PyAMPA: a high-throughput prediction and optimization tool for antimicrobial peptides
Marc Ramos-Llorens, Roberto Bello‐Madruga, Javier Valle, et al.
mSystems (2024) Vol. 9, Iss. 7
Open Access | Times Cited: 3
DeepBP: Ensemble deep learning strategy for bioactive peptide prediction
Ming Zhang, Jianling Zhou, Xiaohua Wang, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 3
Prediction of hemolytic peptides and their hemolytic concentration
Anand Singh Rathore, Nishant Kumar, Shubham Choudhury, et al.
Communications Biology (2025) Vol. 8, Iss. 1
Open Access
Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns
Ahtisham Fazeel Abbasi, Muhammad Nabeel Asim, Sheraz Ahmed, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information
Sen Yang, Piao Xu
Analytical Biochemistry (2024) Vol. 690, pp. 115523-115523
Closed Access | Times Cited: 2
Integrated computational approaches for advancing antimicrobial peptide development
Yanpeng Fang, Yeshuo Ma, Kunqian Yu, et al.
Trends in Pharmacological Sciences (2024) Vol. 45, Iss. 11, pp. 1046-1060
Closed Access | Times Cited: 2
基于机器学习和深度学习的抗菌肽预测研究进展
浩宸 耿
Réngōng zhìnéng qiányán yǔ yìngyòng (2024) Vol. 1, Iss. 1, pp. 54-68
Closed Access | Times Cited: 2
Comparison of deep learning models with simple method to assess the problem of antimicrobial peptides prediction
M. Yu. Lobanov, Mikhail V. Slizen, Nikita V. Dovidchenko, et al.
Molecular Informatics (2023) Vol. 43, Iss. 5
Closed Access | Times Cited: 4
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