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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:

Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein–Ligand Scoring Functions
Chao Yang, Yingkai Zhang
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 11, pp. 2696-2712
Open Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Boosting Protein–Ligand Binding Pose Prediction and Virtual Screening Based on Residue–Atom Distance Likelihood Potential and Graph Transformer
Chao Shen, Xujun Zhang, Yafeng Deng, et al.
Journal of Medicinal Chemistry (2022) Vol. 65, Iss. 15, pp. 10691-10706
Closed Access | Times Cited: 103
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: 75
Artificial Intelligence in Pharmaceutical Sciences
Mingkun Lu, Jiayi Yin, Qi Zhu, et al.
Engineering (2023) Vol. 27, pp. 37-69
Open Access | Times Cited: 57
Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery
Anita Ioana Visan, Irina Neguț
Life (2024) Vol. 14, Iss. 2, pp. 233-233
Open Access | Times Cited: 53
New avenues in artificial-intelligence-assisted drug discovery
Carmen Cerchia, Antonio Lavecchia
Drug Discovery Today (2023) Vol. 28, Iss. 4, pp. 103516-103516
Open Access | Times Cited: 52
Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning
Zhiyong Cui, Ninglong Zhang, Tianxing Zhou, et al.
Journal of Agricultural and Food Chemistry (2023) Vol. 71, Iss. 14, pp. 5630-5645
Closed Access | Times Cited: 33
A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
Chao Shen, Xujun Zhang, Chang‐Yu Hsieh, et al.
Chemical Science (2023) Vol. 14, Iss. 30, pp. 8129-8146
Open Access | Times Cited: 29
Open-Source Machine Learning in Computational Chemistry
Alexander Hagg, Karl N. Kirschner
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 15, pp. 4505-4532
Open Access | Times Cited: 23
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: 23
Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions
Dong Chen, Jian Liu, Guo‐Wei Wei
Nature Machine Intelligence (2024) Vol. 6, Iss. 7, pp. 799-810
Open Access | Times Cited: 9
Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm
Xuehui Fan, Ruixue Ye, Yan Gao, et al.
Frontiers in Artificial Intelligence (2025) Vol. 7
Open Access
Normalized Protein–Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening
Song Xia, Yaowen Gu, Yingkai Zhang
Journal of Chemical Information and Modeling (2025)
Open Access
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
Tobias Harren, Torben Gutermuth, Christoph Grebner, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 3
Closed Access | Times Cited: 5
CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training
Heng Cai, Chao Shen, Tianye Jian, et al.
Chemical Science (2023) Vol. 15, Iss. 4, pp. 1449-1471
Open Access | Times Cited: 11
A new paradigm for applying deep learning to protein–ligand interaction prediction
Zechen Wang, Sheng Wang, Yangyang Li, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 3
PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening
Seokhyun Moon, Sang-Yeon Hwang, Jaechang Lim, et al.
Digital Discovery (2023) Vol. 3, Iss. 2, pp. 287-299
Open Access | Times Cited: 9
Enhancing Generalizability in Protein–Ligand Binding Affinity Prediction with Multimodal Contrastive Learning
Ding Luo, Dandan Liu, Xiaoyang Qu, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 6, pp. 1892-1906
Closed Access | Times Cited: 2
Augmenting Genetic Algorithms with Machine Learning for Inverse Molecular Design
Hannes Kneiding, David Balcells
(2024)
Open Access | Times Cited: 2
Integrating Lean Healthcare and Machine Learning for Cancer Risk Prediction
Mohammad Shahin, Mazdak Maghanaki, F. Frank Chen, et al.
Lecture notes in networks and systems (2024), pp. 373-381
Closed Access | Times Cited: 2
An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
Yanbei Li, Zhehuan Fan, Jingxin Rao, et al.
Medical Review (2023) Vol. 3, Iss. 6, pp. 465-486
Open Access | Times Cited: 7
Template‐guided method for protein–ligand complex structure prediction: Application to CASP15 protein–ligand studies
Xianjin Xu, Rui Duan, Xiaoqin Zou
Proteins Structure Function and Bioinformatics (2023) Vol. 91, Iss. 12, pp. 1829-1836
Open Access | Times Cited: 6
Water Network-Augmented Two-State Model for Protein–Ligand Binding Affinity Prediction
Xiaoyang Qu, Lina Dong, Ding Luo, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 7, pp. 2263-2274
Closed Access | Times Cited: 6
Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules
Xiaoyang Qu, Lina Dong, Xin Zhang, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 18, pp. 4369-4379
Closed Access | Times Cited: 11
Drugging the entire human proteome: Are we there yet?
Micholas Dean Smith, L. Darryl Quarles, Omar Demerdash, et al.
Drug Discovery Today (2024) Vol. 29, Iss. 3, pp. 103891-103891
Open Access | Times Cited: 1
Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery.
Daniel Crusius, Flaviu Cipcigan, Philip C. Biggin
Faraday Discussions (2024)
Open Access | Times Cited: 1
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