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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.

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Showing 1-25 of 121 citing articles:

Structure-based drug design with geometric deep learning
Clemens Isert, Kenneth Atz, Gisbert Schneider
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102548-102548
Open Access | Times Cited: 98
Protein–Ligand Docking in the Machine-Learning Era
Chao Yang, Eric Anthony Chen, Yingkai Zhang
Molecules (2022) Vol. 27, Iss. 14, pp. 4568-4568
Open Access | Times Cited: 86
Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)
Ziduo Yang, Weihe Zhong, Qiujie Lv, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 8, pp. 2020-2033
Closed Access | Times Cited: 57
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Chuang Liu, Yibing Zhan, Jia Wu, et al.
(2023), pp. 6712-6722
Open Access | Times Cited: 48
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Yuchi Qiu, Guo‐Wei Wei
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Open Access | Times Cited: 42
Distributed Graph Neural Network Training: A Survey
Yingxia Shao, Hongzheng Li, Xizhi Gu, et al.
ACM Computing Surveys (2024) Vol. 56, Iss. 8, pp. 1-39
Open Access | Times Cited: 22
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
Graph neural networks in histopathology: Emerging trends and future directions
Siemen Brussee, Giorgio Buzzanca, Anne M.R. Schrader, et al.
Medical Image Analysis (2025) Vol. 101, pp. 103444-103444
Open Access | Times Cited: 3
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction
Shuangli Li, Jingbo Zhou, Tong Xu, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 4, pp. 4541-4549
Open Access | Times Cited: 65
DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug–Target interaction prediction
Peiliang Zhang, Ziqi Wei, Chao Che, et al.
Computers in Biology and Medicine (2022) Vol. 142, pp. 105214-105214
Closed Access | Times Cited: 60
A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
Lilapati Waikhom, Ripon Patgiri
Artificial Intelligence Review (2022) Vol. 56, Iss. 7, pp. 6295-6364
Closed Access | Times Cited: 50
Interaction-aware Drug Package Recommendation via Policy Gradient
Zhi Zheng, Chao Wang, Tong Xu, et al.
ACM transactions on office information systems (2022) Vol. 41, Iss. 1, pp. 1-32
Closed Access | Times Cited: 40
Integrated Molecular Modeling and Machine Learning for Drug Design
Song Xia, Eric Chen, Yingkai Zhang
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 21, pp. 7478-7495
Open Access | Times Cited: 30
Geometric deep learning for drug discovery
Mingquan Liu, Chunyan Li, Ruizhe Chen, et al.
Expert Systems with Applications (2023) Vol. 240, pp. 122498-122498
Closed Access | Times Cited: 27
Calibrated geometric deep learning improves kinase–drug binding predictions
Yunan Luo, Yang Liu, Jian Peng
Nature Machine Intelligence (2023) Vol. 5, Iss. 12, pp. 1390-1401
Open Access | Times Cited: 24
Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning
Clemens Isert, Kenneth Atz, Sereina Riniker, et al.
RSC Advances (2024) Vol. 14, Iss. 7, pp. 4492-4502
Open Access | Times Cited: 12
Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data
Huan Yee Koh, Thi Nguyen, Shirui Pan, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 6, pp. 673-687
Closed Access | Times Cited: 11
Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
ísak Valsson, Matthew T. Warren, Charlotte M. Deane, et al.
Communications Chemistry (2025) Vol. 8, Iss. 1
Open Access | Times Cited: 1
T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment
Gregory W. Kyro, Anthony M. Smaldone, Yu Shee, et al.
Journal of Chemical Information and Modeling (2025)
Open Access | Times Cited: 1
Multiphysical graph neural network (MP-GNN) for COVID-19 drug design
Xiaoshuang Li, Xiang Liu, Le Lü, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Closed Access | Times Cited: 34
Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery
H. Choo, JunJie Wee, Cong Shen, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 10, pp. 2928-2935
Closed Access | Times Cited: 17
Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling
Duanhua Cao, Geng Chen, Jiaxin Jiang, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 6, pp. 688-700
Closed Access | Times Cited: 7
End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins
Tian Cai, Li Xie, Shuo Zhang, et al.
PLoS Computational Biology (2023) Vol. 19, Iss. 1, pp. e1010851-e1010851
Open Access | Times Cited: 14
A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning
Xin Zeng, Shujuan Li, Shuang‐Qing Lv, et al.
Frontiers in Pharmacology (2024) Vol. 15
Open Access | Times Cited: 5
The future of machine learning for small-molecule drug discovery will be driven by data
G. J. DURANT, Fergus Boyles, Kristian Birchall, et al.
Nature Computational Science (2024) Vol. 4, Iss. 10, pp. 735-743
Closed Access | Times Cited: 5
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