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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:

HyperAttentionDTI: improving drug–protein interaction prediction by sequence-based deep learning with attention mechanism
Qichang Zhao, Haochen Zhao, Kai Zheng, et al.
Bioinformatics (2021) Vol. 38, Iss. 3, pp. 655-662
Closed Access | Times Cited: 108

Showing 1-25 of 108 citing articles:

Attention is all you need: utilizing attention in AI-enabled drug discovery
Yang Zhang, Caiqi Liu, Mujiexin Liu, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 102
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: 55
Expanding PROTACtable genome universe of E3 ligases
Yuan Liu, Jingwen Yang, Tianlu Wang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 55
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: 40
Perceiver CPI: a nested cross-attention network for compound–protein interaction prediction
Ngoc-Quang Nguyen, Gwanghoon Jang, Hajung Kim, et al.
Bioinformatics (2022) Vol. 39, Iss. 1
Open Access | Times Cited: 38
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: 24
MCANet: shared-weight-based MultiheadCrossAttention network for drug–target interaction prediction
Jilong Bian, Xi Zhang, Xiying Zhang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 23
GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction
Mengmeng Gao, Daokun Zhang, Yi Chen, et al.
Computers in Biology and Medicine (2024) Vol. 173, pp. 108339-108339
Open Access | Times Cited: 11
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: 9
A review of deep learning methods for ligand based drug virtual screening
Hongjie Wu, Junkai Liu, Runhua Zhang, et al.
Fundamental Research (2024) Vol. 4, Iss. 4, pp. 715-737
Open Access | Times Cited: 9
Advancing drug discovery with deep attention neural networks
Antonio Lavecchia
Drug Discovery Today (2024) Vol. 29, Iss. 8, pp. 104067-104067
Open Access | Times Cited: 9
MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction
Jihong Wang, Ruijia He, Xiaodan Wang, et al.
Molecules (2025) Vol. 30, Iss. 2, pp. 274-274
Open Access
FedKD-CPI: Combining the federated knowledge distillation technique to accomplish synergistic compound-protein interaction prediction
Xuetao Wang, Qichang Zhao, Jianxin Wang
Methods (2025) Vol. 234, pp. 275-283
Closed Access
Predicting drug and target interaction with dilated reparameterize convolution
Min Deng, Jian Wang, Yiming Zhao, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
MPEMDA: A Multi-Similarity Integration Approach with Pre-completion and Error Correction for Predicting Microbe-Drug Associations
Yuxiang Li, Haochen Zhao, Jianxin Wang
Methods (2025)
Closed Access
Deep learning methods for proteome-scale interaction prediction
Min Su Yoon, Byeong‐Soo Bae, Kun Hee Kim, et al.
Current Opinion in Structural Biology (2025) Vol. 90, pp. 102981-102981
Closed Access
MutualDTA: An Interpretable Drug–Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention
Yongna Yuan, Siming Chen, Rizhen Hu, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network
Shuang‐Qing Lv, Xin Zeng, Guang-Peng Su, et al.
Biomolecules (2025) Vol. 15, Iss. 2, pp. 221-221
Open Access
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: 33
DTITR: End-to-end drug–target binding affinity prediction with transformers
Nelson R. C. Monteiro, José Luís Oliveira, Joel P. Arrais
Computers in Biology and Medicine (2022) Vol. 147, pp. 105772-105772
Open Access | Times Cited: 29
Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug–drug interactions prediction
Xudong Zhang, Gan Wang, Xiangyu Meng, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 29
Generative models for protein sequence modeling: recent advances and future directions
Mehrsa Mardikoraem, Zirui Wang, Nathaniel Pascual, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Open Access | Times Cited: 19
MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention
Wenchuan Zhao, Yufeng Yu, Guosheng Liu, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 4
NCH-DDA: Neighborhood contrastive learning heterogeneous network for drug–disease association prediction
Peiliang Zhang, Chao Che, Bo Jin, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121855-121855
Closed Access | Times Cited: 11
Neuromorphic computing for modeling neurological and psychiatric disorders: implications for drug development
Amisha S. Raikar, J.H. Andrew, Pranjali Prabhu Dessai, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 12
Open Access | Times Cited: 4
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