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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 approaches and databases for prediction of drug–target interaction: a survey paper
Maryam Bagherian, Elyas Sabeti, Kai Wang, et al.
Briefings in Bioinformatics (2019) Vol. 22, Iss. 1, pp. 247-269
Open Access | Times Cited: 334

Showing 1-25 of 334 citing articles:

Towards the sustainable discovery and development of new antibiotics
Marcus Miethke, Marco Pieroni, Tilmann Weber, et al.
Nature Reviews Chemistry (2021) Vol. 5, Iss. 10, pp. 726-749
Open Access | Times Cited: 797
Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Lalitkumar K. Vora, Amol D. Gholap, Keshava Jetha, et al.
Pharmaceutics (2023) Vol. 15, Iss. 7, pp. 1916-1916
Open Access | Times Cited: 393
Use of molecular docking computational tools in drug discovery
Francesca Stanzione, Ilenia Giangreco, Jason C. Cole
Progress in medicinal chemistry (2021), pp. 273-343
Closed Access | Times Cited: 372
m6A modification: recent advances, anticancer targeted drug discovery and beyond
Lijuan Deng, Wei-Qing Deng, Shu-Ran Fan, et al.
Molecular Cancer (2022) Vol. 21, Iss. 1
Open Access | Times Cited: 255
The role of machine learning in clinical research: transforming the future of evidence generation
E. Hope Weissler, Tristan Naumann, Tomas Andersson, et al.
Trials (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 193
A unified drug–target interaction prediction framework based on knowledge graph and recommendation system
Qing Ye, Chang‐Yu Hsieh, Ziyi Yang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 157
PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions
Seokhyun Moon, Wonho Zhung, Soojung Yang, et al.
Chemical Science (2022) Vol. 13, Iss. 13, pp. 3661-3673
Open Access | Times Cited: 121
Artificial intelligence for drug discovery: Resources, methods, and applications
Wei Chen, Xuesong Liu, Sanyin Zhang, et al.
Molecular Therapy — Nucleic Acids (2023) Vol. 31, pp. 691-702
Open Access | Times Cited: 117
Contrastive learning in protein language space predicts interactions between drugs and protein targets
Rohit Singh, Samuel Sledzieski, Bryan D. Bryson, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 24
Open Access | Times Cited: 85
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Stephen Bonner, Ian P. Barrett, Cheng Ye, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Open Access | Times Cited: 84
Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery
Ri Han, Hongryul Yoon, Gahee Kim, et al.
Pharmaceuticals (2023) Vol. 16, Iss. 9, pp. 1259-1259
Open Access | Times Cited: 70
Artificial Intelligence in Pharmaceutical Sciences
Mingkun Lu, Jiayi Yin, Qi Zhu, et al.
Engineering (2023) Vol. 27, pp. 37-69
Open Access | Times Cited: 64
iGRLDTI: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network
Bo-Wei Zhao, Xiaorui Su, Pengwei Hu, et al.
Bioinformatics (2023) Vol. 39, Iss. 8
Open Access | Times Cited: 57
AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
Hongjie Wu, Junkai Liu, Tengsheng Jiang, et al.
Neural Networks (2023) Vol. 169, pp. 623-636
Open Access | Times Cited: 54
Discovering small-molecule senolytics with deep neural networks
Felix Wong, Satotaka Omori, Nina M. Donghia, et al.
Nature Aging (2023) Vol. 3, Iss. 6, pp. 734-750
Closed Access | Times Cited: 42
CCL-DTI: contributing the contrastive loss in drug–target interaction prediction
Alireza Dehghan, Karim Abbasi, Parvin Razzaghi, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 33
Artificial intelligence in drug development
Kang Zhang, Xin Yang, Yifei Wang, et al.
Nature Medicine (2025) Vol. 31, Iss. 1, pp. 45-59
Closed Access | Times Cited: 11
Artificial intelligence streamlines scientific discovery of drug–target interactions
Yuxin Yang, Feixiong Cheng
British Journal of Pharmacology (2025)
Open Access | Times Cited: 2
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction
Jiajie Peng, Yuxian Wang, Jiaojiao Guan, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 5
Closed Access | Times Cited: 134
Comprehensive Survey of Recent Drug Discovery Using Deep Learning
Jintae Kim, Sera Park, Dongbo Min, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 18, pp. 9983-9983
Open Access | Times Cited: 99
A review on compound-protein interaction prediction methods: Data, format, representation and model
Sangsoo Lim, Yijingxiu Lu, Chang Yun Cho, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 1541-1556
Open Access | Times Cited: 90
Machine learning for perturbational single-cell omics
Yuge Ji, Mohammad Lotfollahi, Florian Wolf, et al.
Cell Systems (2021) Vol. 12, Iss. 6, pp. 522-537
Open Access | Times Cited: 85
Deep drug-target binding affinity prediction with multiple attention blocks
Yuni Zeng, Xiangru Chen, Yujie Luo, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Open Access | Times Cited: 82
DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network
Cheng Chen, Shi Han, Zhiwen Jiang, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104676-104676
Open Access | Times Cited: 73
ML-DTI: Mutual Learning Mechanism for Interpretable Drug–Target Interaction Prediction
Ziduo Yang, Weihe Zhong, Lu Zhao, et al.
The Journal of Physical Chemistry Letters (2021) Vol. 12, Iss. 17, pp. 4247-4261
Closed Access | Times Cited: 72
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