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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:

Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
QHwan Kim, Joon-Hyuk Ko, Sunghoon Kim, et al.
Bioinformatics (2021) Vol. 37, Iss. 20, pp. 3428-3435
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

A survey of uncertainty in deep neural networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. S1, pp. 1513-1589
Open Access | Times Cited: 476
Sequence-based drug design as a concept in computational drug design
Lifan Chen, Zisheng Fan, Jie Chang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 60
Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction
Yunjiang Zhang, Shuyuan Li, Kong Meng, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 5, pp. 1456-1472
Closed Access | Times Cited: 19
BridgeDPI: a novel Graph Neural Network for predicting drug–protein interactions
Yifan Wu, Min Gao, Min Zeng, et al.
Bioinformatics (2022) Vol. 38, Iss. 9, pp. 2571-2578
Open Access | Times Cited: 63
Artificial intelligence for compound pharmacokinetics prediction
Olga Obrezanova
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102546-102546
Closed Access | Times Cited: 33
A survey of drug-target interaction and affinity prediction methods via graph neural networks
Yue Zhang, Yuqing Hu, Na Han, et al.
Computers in Biology and Medicine (2023) Vol. 163, pp. 107136-107136
Closed Access | Times Cited: 26
Food-derived bioactive peptides: The gateway to reach the full potential of food proteins for human health
Ilekuttige Priyan Shanura Fernando, Jianping Wu
Trends in Food Science & Technology (2025) Vol. 157, pp. 104896-104896
Closed Access | Times Cited: 1
Enhancing uncertainty quantification in drug discovery with censored regression labels
Emma Svensson, Hannah Rosa Friesacher, Susanne Winiwarter, et al.
Artificial Intelligence in the Life Sciences (2025), pp. 100128-100128
Open Access | Times Cited: 1
A brief review of protein–ligand interaction prediction
Lingling Zhao, Yan Zhu, Junjie Wang, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 2831-2838
Open Access | Times Cited: 34
BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification
Shuyu Wang, Hongzhou Tang, Yuliang Zhao, et al.
Protein Science (2022) Vol. 31, Iss. 11
Open Access | Times Cited: 32
Multi-modality attribute learning-based method for drug–protein interaction prediction based on deep neural network
Weihe Dong, Qiang Yang, Jian Wang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 17
PMF-CPI: assessing drug selectivity with a pretrained multi-functional model for compound–protein interactions
Nan Song, Ruihan Dong, Yuqian Pu, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 16
Uncertainty quantification: Can we trust artificial intelligence in drug discovery?
Jie Yu, Dingyan Wang, Mingyue Zheng
iScience (2022) Vol. 25, Iss. 8, pp. 104814-104814
Open Access | Times Cited: 24
Uncertainty-guided Active Learning with theoretical bounds on label complexity
Deepesh Agarwal, Balasubramaniam Natarajan
ACM transactions on probabilistic machine learning. (2025)
Closed Access
BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers
Hyunho Kim, Minsu Park, Ingoo Lee, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Closed Access | Times Cited: 19
A spectrum of explainable and interpretable machine learning approaches for genomic studies
Ashley Mae Conard, Alan DenAdel, Lorin Crawford
Wiley Interdisciplinary Reviews Computational Statistics (2023) Vol. 15, Iss. 5
Open Access | Times Cited: 11
Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models
Hannah Rosa Friesacher, Ola Engkvist, Lewis Mervin, et al.
Journal of Cheminformatics (2025) Vol. 17, Iss. 1
Open Access
PT-Finder: A multi-modal neural network approach to target identification
Hossam Nada, Sung-Do Kim, Kyeong Lee
Computers in Biology and Medicine (2024) Vol. 174, pp. 108444-108444
Closed Access | Times Cited: 3
Reducing overconfident errors in molecular property classification using Posterior Network
Zhehuan Fan, Jie Yu, Xiang Zhang, et al.
Patterns (2024) Vol. 5, Iss. 6, pp. 100991-100991
Open Access | Times Cited: 2
Self- and cross-attention accurately predicts metabolite–protein interactions
Pedro Alonso Campana, Zoran Nikoloski
NAR Genomics and Bioinformatics (2023) Vol. 5, Iss. 1
Open Access | Times Cited: 6
Drug-Target Interactions Prediction at Scale: the Komet Algorithm with the LCIdb Dataset
Gwenn Guichaoua, Philippe Pinel, Brice Hoffmann, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction
Lijun Cai, Guanghui Yue, Yifan Chen, et al.
Briefings in Bioinformatics (2024) Vol. 26, Iss. 1
Open Access | Times Cited: 1
Prediction of drug–protein interaction based on dual channel neural networks with attention mechanism
Dayu Tan, Haijun Jiang, Haitao Li, et al.
Briefings in Functional Genomics (2023) Vol. 23, Iss. 3, pp. 286-294
Closed Access | Times Cited: 2
SYSTCM: A systemic web platform for objective identification of pharmacological effects based on interplay of “traditional Chinese Medicine-components-targets”
Zewen Wang, Mengqi Huo, Liansheng Qiao, et al.
Computers in Biology and Medicine (2024) Vol. 179, pp. 108878-108878
Closed Access
Drug–Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset
Gwenn Guichaoua, Philippe Pinel, Brice Hoffmann, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 18, pp. 6938-6956
Open Access
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