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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:

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer
Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, et al.
(2020), pp. 1-10
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search
Diogo Santos‐Martins, Leonardo Solis-Vasquez, Andreas F. Tillack, et al.
Journal of Chemical Theory and Computation (2021) Vol. 17, Iss. 2, pp. 1060-1073
Open Access | Times Cited: 239
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
Atanu Acharya, Rupesh Agarwal, Matthew Baker, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 5832-5852
Open Access | Times Cited: 165
The transformational role of GPU computing and deep learning in drug discovery
Mohit Pandey, Michael Fernández, Francesco Gentile, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 3, pp. 211-221
Open Access | Times Cited: 162
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Alexander Tropsha, Olexandr Isayev, Alexandre Varnek, et al.
Nature Reviews Drug Discovery (2023) Vol. 23, Iss. 2, pp. 141-155
Closed Access | Times Cited: 91
Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies
Davide Bassani, Stefano Moro
Molecules (2023) Vol. 28, Iss. 9, pp. 3906-3906
Open Access | Times Cited: 53
Accelerating AutoDock Vina with GPUs
Shidi Tang, Ruiqi Chen, Mengru Lin, et al.
Molecules (2022) Vol. 27, Iss. 9, pp. 3041-3041
Open Access | Times Cited: 65
A Review on Parallel Virtual Screening Softwares for High-Performance Computers
N. Arul Murugan, Artur Podobas, Davide Gadioli, et al.
Pharmaceuticals (2022) Vol. 15, Iss. 1, pp. 63-63
Open Access | Times Cited: 47
Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches
Faheem Ahmed, In Suk Kang, Kyung Hwan Kim, et al.
Journal of Medical Virology (2023) Vol. 95, Iss. 4
Closed Access | Times Cited: 36
High-throughput virtual laboratory for drug discovery using massive datasets
Jens Gläser, Josh V. Vermaas, David Rogers, et al.
The International Journal of High Performance Computing Applications (2021) Vol. 35, Iss. 5, pp. 452-468
Closed Access | Times Cited: 42
AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection
Austin Clyde, Xuefeng Liu, Thomas Brettin, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
High performance-oriented computer aided drug design approaches in the exascale era
Andrea Rizzi, Davide Mandelli
Expert Opinion on Drug Discovery (2025)
Closed Access
Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review
Argus Sun, Tyler Hoffman, Bao Q. Luu, et al.
Bio-Design and Manufacturing (2021) Vol. 4, Iss. 4, pp. 757-775
Open Access | Times Cited: 37
SARS-CoV2 billion-compound docking
David Rogers, Rupesh Agarwal, Josh V. Vermaas, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 14
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
Aymen Al Saadi, Dario Alfè, Yadu Babuji, et al.
(2021)
Open Access | Times Cited: 24
EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2
Davide Gadioli, Emanuele Vitali, Federico Ficarelli, et al.
IEEE Transactions on Emerging Topics in Computing (2022) Vol. 11, Iss. 1, pp. 170-181
Open Access | Times Cited: 17
The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
Manna Dai, Gao Xiao, Ming Shao, et al.
Biosensors (2023) Vol. 13, Iss. 3, pp. 389-389
Open Access | Times Cited: 9
Supercomputing Pipelines Search for Therapeutics Against COVID-19
Josh V. Vermaas, Ada Sedova, Matthew Baker, et al.
Computing in Science & Engineering (2020) Vol. 23, Iss. 1, pp. 7-16
Open Access | Times Cited: 26
Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
Abdul Majeed, Sungchang Lee
Applied System Innovation (2021) Vol. 4, Iss. 3, pp. 40-40
Open Access | Times Cited: 15
Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores
Gabin Schieffer, Ivy Bo Peng
Lecture notes in computer science (2023), pp. 608-622
Closed Access | Times Cited: 5
A GPU-Accelerated Molecular Docking Workflow with Kubernetes and Apache Airflow
Daniel Medeiros, Gabin Schieffer, Jacob Wahlgren, et al.
Lecture notes in computer science (2023), pp. 193-206
Closed Access | Times Cited: 5
Computational Approaches and Drug Discovery: Where Are We Going?
Marco Tutone, Anna Maria Almerico
Molecules (2024) Vol. 29, Iss. 5, pp. 969-969
Open Access | Times Cited: 1
High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function
Mu Gao, Peik K. Lund-Andersen, Alex Morehead, et al.
(2021), pp. 46-57
Open Access | Times Cited: 12
Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets
Kadir Kocabaş, Alina Arif, Reaz Uddin, et al.
PLoS ONE (2022) Vol. 17, Iss. 5, pp. e0268889-e0268889
Open Access | Times Cited: 8
Graphene oxide immobilized 2-morpholinoethanamine as a versatile acid–base catalyst for synthesis of some heterocyclic compounds and molecular docking study
Leila Amiri‐Zirtol, Tahereh Solymani Ahooie, Elham Riazimontazer, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 4
Understanding the I/O Impact on the Performance of High-Throughput Molecular Docking
Stefano Markidis, Davide Gadioli, Emanuele Vitali, et al.
(2021), pp. 9-14
Open Access | Times Cited: 11
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