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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:

Deep learning for low-data drug discovery: Hurdles and opportunities
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, et al.
Current Opinion in Structural Biology (2024) Vol. 86, pp. 102818-102818
Open Access | Times Cited: 11

Showing 11 citing articles:

Advances in bioinformatic methods for the acceleration of the drug discovery from nature
Magdalena Maciejewska‐Turska, Milen I. Georgiev, Guoyin Kai, et al.
Phytomedicine (2025) Vol. 139, pp. 156518-156518
Closed Access
Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery
Maximilian G. Schuh, Davide Boldini, Stephan A. Sieber
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 12, pp. 4640-4650
Open Access | Times Cited: 4
Traversing chemical space with active deep learning for low-data drug discovery
Derek van Tilborg, Francesca Grisoni
Nature Computational Science (2024)
Closed Access | Times Cited: 3
Traversing Chemical Space with Active Deep Learning: A Computational Framework for Low-data Drug Discovery
Derek van Tilborg, Francesca Grisoni
(2024)
Open Access | Times Cited: 2
Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction
Xinkang Li, Feng Zhang, Liangzhen Zheng, et al.
Journal of Hazardous Materials (2024) Vol. 475, pp. 134828-134828
Closed Access | Times Cited: 1
Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery
Leticia Manen-Freixa, Albert A. Antolín
Expert Opinion on Drug Discovery (2024) Vol. 19, Iss. 9, pp. 1043-1069
Closed Access | Times Cited: 1
Finding the most potent compounds using active learning on molecular pairs
Zachary Fralish, Daniel Reker
Beilstein Journal of Organic Chemistry (2024) Vol. 20, pp. 2152-2162
Open Access | Times Cited: 1
A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction
Rıza Özçelik, Francesca Grisoni
Digital Discovery (2024)
Open Access | Times Cited: 1
Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning
Banu İsmail Mendi, Hatice Şanlı, Mert Akın İnsel, et al.
Life (2024) Vol. 14, Iss. 11, pp. 1371-1371
Open Access
Beyond the Hype: The Reality of Implementing AI to Drive Drug Discovery Success
Matthew Segall
GEN Biotechnology (2024)
Closed Access
The predictivity of QSARs for toxicity: Recommendations for improving model performance
Mark T.D. Cronin, Hassan Basiri, Georgios Chrysochoou, et al.
Computational Toxicology (2024) Vol. 33, pp. 100338-100338
Open Access
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