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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:

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 12

Showing 12 citing articles:

Deep-ProBind: binding protein prediction with transformer-based deep learning model
Salman Khan, Sumaiya Noor, Hamid Hussain Awan, et al.
BMC Bioinformatics (2025) Vol. 26, Iss. 1
Open Access | Times Cited: 1
LMCrot: an enhanced protein crotonylation site predictor by leveraging an interpretable window-level embedding from a transformer-based protein language model
Pawel Pratyush, Soufia Bahmani, Suresh Pokharel, et al.
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 6
ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites
Van‐The Le, Zijun Zhan, Thi-Thu-Phuong Vu, et al.
Journal of Molecular Graphics and Modelling (2024) Vol. 130, pp. 108777-108777
Closed Access | Times Cited: 5
PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model
Junxiong Huang, Weikang Li, Bin Xiao, et al.
iScience (2024) Vol. 27, Iss. 10, pp. 110850-110850
Open Access | Times Cited: 2
Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
Sayed Mehedi Azim, Aravind Balasubramanyam, Sheikh Rabiul Islam, et al.
Algorithms (2024) Vol. 17, Iss. 9, pp. 409-409
Open Access | Times Cited: 2
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: 2
Growing ecosystem of deep learning methods for modeling protein–protein interactions
Julia R. Rogers, Gergő Nikolényi, Mohammed AlQuraishi
Protein Engineering Design and Selection (2023) Vol. 36
Open Access | Times Cited: 6
DP-site: A dual deep learning-based method for protein-peptide interaction site prediction
Shima Shafiee, Abdolhossein Fathi, Ghazaleh Taherzadeh
Methods (2024) Vol. 229, pp. 17-29
Closed Access | Times Cited: 1
GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach
Cheng Zhu, Chengyun Zhang, Tianfeng Shang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access
PepPFN: protein-peptide binding residues prediction via pre-trained module-based Fourier Network
Xue Li, Ben Cao, Hongzhen Ding, et al.
(2024), pp. 1075-1080
Closed Access
Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation
Katarina Vrbnjak, Raj Nayan Sewduth
Pharmaceutics (2024) Vol. 16, Iss. 11, pp. 1486-1486
Open Access
Leveraging large language models for peptide antibiotic design
Changge Guan, Fabiano C. Fernandes, Octávio Luiz Franco, et al.
Cell Reports Physical Science (2024) Vol. 6, Iss. 1, pp. 102359-102359
Open Access
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