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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:

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
Chao Shen, Gaoqi Weng, Xujun Zhang, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 34

Showing 1-25 of 34 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: 112
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: 83
Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities
Ruby Srivastava
ACS Omega (2021) Vol. 6, Iss. 38, pp. 24891-24901
Open Access | Times Cited: 99
Synthesis, vibrational analysis, molecular property investigation, and molecular docking of new benzenesulphonamide-based carboxamide derivatives against Plasmodium falciparum
David Izuchukwu Ugwu, Fredrick C. Asogwa, Hitler Louis, et al.
Journal of Molecular Structure (2022) Vol. 1269, pp. 133796-133796
Closed Access | Times Cited: 50
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: 47
Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening
Clara Blanes‐Mira, Pilar Fernández-Aguado, Jorge de Andrés-López, et al.
Molecules (2022) Vol. 28, Iss. 1, pp. 175-175
Open Access | Times Cited: 41
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
Zechen Wang, Liangzhen Zheng, Sheng Wang, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Open Access | Times Cited: 39
A practical guide to machine-learning scoring for structure-based virtual screening
Viet‐Khoa Tran‐Nguyen, Muhammad Junaid, Saw Simeon, et al.
Nature Protocols (2023) Vol. 18, Iss. 11, pp. 3460-3511
Closed Access | Times Cited: 38
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: 35
Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons
Viet‐Khoa Tran‐Nguyen, Pedro J. Ballester
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 5, pp. 1401-1405
Open Access | Times Cited: 16
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: 6
Featurization strategies for protein–ligand interactions and their applications in scoring function development
Guo‐Li Xiong, Chao Shen, Ziyi Yang, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 12, Iss. 2
Closed Access | Times Cited: 39
The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction
Chao Shen, Xueping Hu, Junbo Gao, et al.
Journal of Cheminformatics (2021) Vol. 13, Iss. 1
Open Access | Times Cited: 39
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors
Klaudia Caba, Viet‐Khoa Tran‐Nguyen, Taufiq Rahman, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 5
BoostSF-SHAP: Gradient boosting-based software for protein–ligand binding affinity prediction with explanations
Xingqian Chen, Shuangbao Song, Zhenyu Song, et al.
Neurocomputing (2025), pp. 129303-129303
Closed Access
Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises
Natesh Singh, Bruno O. Villoutreix
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 2537-2548
Open Access | Times Cited: 28
TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions
Xujun Zhang, Chao Shen, Ben Liao, et al.
Journal of Medicinal Chemistry (2022) Vol. 65, Iss. 11, pp. 7918-7932
Closed Access | Times Cited: 22
Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking
Jingyu Zhu, Huiqin Meng, Xintong Li, et al.
Computational Biology and Chemistry (2024) Vol. 109, pp. 108011-108011
Closed Access | Times Cited: 4
TB-IECS: an accurate machine learning-based scoring function for virtual screening
Xujun Zhang, Chao Shen, Dejun Jiang, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 10
Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies
Seokhyun Moon, Wonho Zhung, Woo Youn Kim
Wiley Interdisciplinary Reviews Computational Molecular Science (2024) Vol. 14, Iss. 1
Closed Access | Times Cited: 3
Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern
Gabriel Corrêa Veríssimo, Mateus Sá Magalhães Serafim, Thales Kronenberger, et al.
Expert Opinion on Drug Discovery (2022) Vol. 17, Iss. 9, pp. 929-947
Closed Access | Times Cited: 14
Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA
Natalia A. Szulc, Zuzanna Mackiewicz, Janusz M. Bujnicki, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Open Access | Times Cited: 8
Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction
Jie Li, Xingyi Guan, Oufan Zhang, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 8
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
Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions
Viet‐Khoa Tran‐Nguyen, Saw Simeon, Muhammad Junaid, et al.
Current Research in Structural Biology (2022) Vol. 4, pp. 206-210
Open Access | Times Cited: 9
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