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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:

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks
Patrick Brendan Timmons, Chandralal M. Hewage
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 118

Showing 1-25 of 118 citing articles:

Accelerating antibiotic discovery through artificial intelligence
Marcelo C. R. Melo, Jacqueline R. M. A. Maasch, César de la Fuente‐Núñez
Communications Biology (2021) Vol. 4, Iss. 1
Open Access | Times Cited: 154
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: 84
Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics
Panchali Barman, Shubhi Joshi, Sheetal Sharma, et al.
International Journal of Peptide Research and Therapeutics (2023) Vol. 29, Iss. 4
Open Access | Times Cited: 63
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
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
Jing Xu, Fuyi Li, Chen Li, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Open Access | Times Cited: 42
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
Machine learning-guided discovery and design of non-hemolytic peptides
Fabien Plisson, Obed Ramírez‐Sánchez, Cristina Martínez-Hernández
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 125
Cyanobacteria and Eukaryotic Microalgae as Emerging Sources of Antibacterial Peptides
Verónica Rojas, Luís Rivas, Constanza Cárdenas, et al.
Molecules (2020) Vol. 25, Iss. 24, pp. 5804-5804
Open Access | Times Cited: 88
ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides
Patrick Brendan Timmons, Chandralal M. Hewage
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 67
Review and perspective on bioactive peptides: A roadmap for research, development, and future opportunities
Zhenjiao Du, Yonghui Li
Journal of Agriculture and Food Research (2022) Vol. 9, pp. 100353-100353
Open Access | Times Cited: 65
Machine Learning Prediction of Antimicrobial Peptides
Guangshun Wang, Iosif I. Vaisman, Monique L. van Hoek
Methods in molecular biology (2022), pp. 1-37
Open Access | Times Cited: 60
MLACP 2.0: An updated machine learning tool for anticancer peptide prediction
Le Thi Phan, Hyun Woo Park, Thejkiran Pitti, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 4473-4480
Open Access | Times Cited: 49
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: 44
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: 30
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
Optimizing molecules using efficient queries from property evaluations
Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, et al.
Nature Machine Intelligence (2021) Vol. 4, Iss. 1, pp. 21-31
Open Access | Times Cited: 46
Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM
Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 46
APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures
Patrick Brendan Timmons, Chandralal M. Hewage
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 41
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
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: 22
Antibacterial and Antiviral Properties of Chenopodin-Derived Synthetic Peptides
Marcia L. Feijoo-Coronel, Bruno Mendes, David Ramírez, et al.
Antibiotics (2024) Vol. 13, Iss. 1, pp. 78-78
Open Access | Times Cited: 8
Peptides in chemical space
Alice Capecchi, Jean‐Louis Reymond
Medicine in Drug Discovery (2021) Vol. 9, pp. 100081-100081
Open Access | Times Cited: 37
An in silico scientific basis for LL‐37 as a therapeutic for Covid‐19
Kiran Bharat Lokhande, Tanushree Banerjee, K. Venkateswara Swamy, et al.
Proteins Structure Function and Bioinformatics (2021) Vol. 90, Iss. 5, pp. 1029-1043
Open Access | Times Cited: 33
Machine Learning Guided Discovery of Non‐Hemolytic Membrane Disruptive Anticancer Peptides
E. Yu. Zakharova, Markus Orsi, Alice Capecchi, et al.
ChemMedChem (2022) Vol. 17, Iss. 17
Open Access | Times Cited: 26
Arginine and tryptophan-rich dendritic antimicrobial peptides that disrupt membranes for bacterial infection in vivo
Yinyin Yang, Zhongwei Yu, Zufang Ba, et al.
European Journal of Medicinal Chemistry (2024) Vol. 271, pp. 116451-116451
Closed Access | Times Cited: 5
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