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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:

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features
Agamemnon Krasoulis, Nick Antonopoulos, Vassilis Pitsikalis, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 19, pp. 4642-4659
Open Access | Times Cited: 12

Showing 12 citing articles:

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
Liuying Wang, Yongzhen Song, Hesong Wang, et al.
Pharmaceuticals (2023) Vol. 16, Iss. 2, pp. 253-253
Open Access | Times Cited: 51
Neural multi-task learning in drug design
Stephan Allenspach, Jan A. Hiss, Gisbert Schneider
Nature Machine Intelligence (2024) Vol. 6, Iss. 2, pp. 124-137
Closed Access | Times Cited: 9
In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back
Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García, et al.
Advanced Materials (2024) Vol. 36, Iss. 30
Closed Access | Times Cited: 8
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
Virtual Screening of Gum Arabic Compounds for Breast Cancer-Related Receptors: Target Selection and Therapeutic Potential
Ahmed A. M. Elnour, Ashraf Nour, Yasser S. Ahmed, et al.
(2025), pp. 307-336
Closed Access
A review on graph neural networks for predicting synergistic drug combinations
Milad Besharatifard, Fatemeh Vafaee
Artificial Intelligence Review (2024) Vol. 57, Iss. 3
Open Access | Times Cited: 4
BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening
Michael Brocidiacono, Paul Francoeur, Rishal Aggarwal, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 7, pp. 2488-2495
Closed Access | Times Cited: 7
Prospects and Challenges of Deep Learning in Gynaecological Malignancies
Yingfeng Zhang, Liqiong Zeng
(2024)
Closed Access
Artificial Intelligence–Driven Computational Approaches in the Development of Anticancer Drugs
Pankaj Garg, G. D. Singhal, Prakash Kulkarni, et al.
Cancers (2024) Vol. 16, Iss. 22, pp. 3884-3884
Open Access
HiTViSc: High-Throughput Virtual Screening as a Service
Natalia Nikitina, Evgeny Ivashko
Lecture notes in computer science (2023), pp. 83-92
Closed Access | Times Cited: 1
A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations
Milad Besharatifard, Fatemeh Vafaee
Research Square (Research Square) (2023)
Open Access
Utilizing AI and Nanotechnology Solutions to Propel Progress in Cancer Diagnostics, Therapeutics, and Integrated Theranostics
Z. Saleh, Wassim Jaber, Ali Jaber, et al.
Advances in computational intelligence and robotics book series (2023), pp. 114-135
Closed Access
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