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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:

Chemical language models enable navigation in sparsely populated chemical space
Michael A. Skinnider, R. Greg Stacey, David S. Wishart, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 9, pp. 759-770
Closed Access | Times Cited: 112

Showing 1-25 of 112 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 702
Geometric deep learning on molecular representations
Kenneth Atz, Francesca Grisoni, Gisbert Schneider
Nature Machine Intelligence (2021) Vol. 3, Iss. 12, pp. 1023-1032
Closed Access | Times Cited: 238
High-confidence structural annotation of metabolites absent from spectral libraries
Martin Hoffmann, Louis‐Félix Nothias, Marcus Ludwig, et al.
Nature Biotechnology (2021) Vol. 40, Iss. 3, pp. 411-421
Open Access | Times Cited: 180
Deep learning approaches for de novo drug design: An overview
Mingyang Wang, Zhe Wang, Huiyong Sun, et al.
Current Opinion in Structural Biology (2021) Vol. 72, pp. 135-144
Closed Access | Times Cited: 113
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
Michaël Moret, Irène Pachón-Angona, Leandro Cotos, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 93
Chemical language models for de novo drug design: Challenges and opportunities
Francesca Grisoni
Current Opinion in Structural Biology (2023) Vol. 79, pp. 102527-102527
Open Access | Times Cited: 61
Functional annotation of enzyme-encoding genes using deep learning with transformer layers
Gi Bae Kim, Ji Yeon Kim, Jong An Lee, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 53
Neural scaling of deep chemical models
Nathan C. Frey, Ryan Soklaski, Simon Axelrod, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 11, pp. 1297-1305
Open Access | Times Cited: 48
Reinvent 4: Modern AI–driven generative molecule design
Hannes H. Loeffler, Jiazhen He, Alessandro Tibo, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 48
Machine learning in preclinical drug discovery
Denise B. Catacutan, Jeremie Alexander, Autumn Arnold, et al.
Nature Chemical Biology (2024) Vol. 20, Iss. 8, pp. 960-973
Closed Access | Times Cited: 36
Prospective de novo drug design with deep interactome learning
Kenneth Atz, Leandro Cotos, Clemens Isert, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 31
Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
Amit Gangwal, M. Azim Ansari, Iqrar Ahmad, et al.
Frontiers in Pharmacology (2024) Vol. 15
Open Access | Times Cited: 25
Invalid SMILES are beneficial rather than detrimental to chemical language models
Michael A. Skinnider
Nature Machine Intelligence (2024) Vol. 6, Iss. 4, pp. 437-448
Open Access | Times Cited: 17
Artificial intelligence in drug development
Kang Zhang, Xin Yang, Yifei Wang, et al.
Nature Medicine (2025) Vol. 31, Iss. 1, pp. 45-59
Closed Access | Times Cited: 11
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: 28
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity
Toshiki Ochiai, Tensei Inukai, Manato Akiyama, et al.
Communications Chemistry (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 22
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: 12
Chemical language modeling with structured state space sequence models
Rıza Özçelik, Sarah de Ruiter, Emanuele Criscuolo, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Coverage bias in small molecule machine learning
Fleming Kretschmer, Jan Seipp, Marcus Ludwig, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 1
fragSMILES as a chemical string notation for advanced fragment and chirality representation
Fabrizio Mastrolorito, Fulvio Ciriaco, Maria Vittoria Togo, et al.
Communications Chemistry (2025) Vol. 8, Iss. 1
Open Access | Times Cited: 1
A deep generative model enables automated structure elucidation of novel psychoactive substances
Michael A. Skinnider, Fei Wang, Daniel Pasin, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 11, pp. 973-984
Closed Access | Times Cited: 54
Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly
Seonghwan Seo, Jaechang Lim, Woo Youn Kim
Advanced Science (2023) Vol. 10, Iss. 8
Open Access | Times Cited: 19
67 million natural product-like compound database generated via molecular language processing
Dillon W. P. Tay, Naythan Yeo, Krishnan Adaikkappan, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 19
Exploring chemical space — Generative models and their evaluation
Martin Vogt
Artificial Intelligence in the Life Sciences (2023) Vol. 3, pp. 100064-100064
Open Access | Times Cited: 18
REINVENT4: Modern AI–Driven Generative Molecule Design
Hannes H. Loeffler, Jiazhen He, Alessandro Tibo, et al.
(2023)
Open Access | Times Cited: 16
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