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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:

Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
W. Patrick Walters, Regina Barzilay
Accounts of Chemical Research (2020) Vol. 54, Iss. 2, pp. 263-270
Closed Access | Times Cited: 236

Showing 1-25 of 236 citing articles:

On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1476
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Haoxin Mai, Tu C. Le, Dehong Chen, et al.
Chemical Reviews (2022) Vol. 122, Iss. 16, pp. 13478-13515
Closed Access | Times Cited: 236
Generative models for molecular discovery: Recent advances and challenges
Camille L. Bilodeau, Wengong Jin, Tommi Jaakkola, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Closed Access | Times Cited: 187
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, et al.
Patterns (2022) Vol. 3, Iss. 10, pp. 100588-100588
Open Access | Times Cited: 143
A review on machine learning algorithms for the ionic liquid chemical space
Spyridon Koutsoukos, Frederik Philippi, Francisco Malaret, et al.
Chemical Science (2021) Vol. 12, Iss. 20, pp. 6820-6843
Open Access | Times Cited: 118
A Guide to In Silico Drug Design
Yiqun Chang, Bryson A. Hawkins, Jonathan J. Du, et al.
Pharmaceutics (2022) Vol. 15, Iss. 1, pp. 49-49
Open Access | Times Cited: 105
Accelerated rational PROTAC design via deep learning and molecular simulations
Shuangjia Zheng, Youhai Tan, Zhenyu Wang, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 9, pp. 739-748
Closed Access | Times Cited: 64
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 4, pp. 340-350
Open Access | Times Cited: 53
Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches
Ajay Vikram Singh, Mansi Varma, Mansi Rai, et al.
Advanced Intelligent Systems (2024) Vol. 6, Iss. 4
Closed Access | Times Cited: 31
Self-Driving Laboratories for Chemistry and Materials Science
Gary Tom, Stefan P. Schmid, Sterling G. Baird, et al.
Chemical Reviews (2024) Vol. 124, Iss. 16, pp. 9633-9732
Open Access | Times Cited: 18
Recent advancements in carbonic anhydrase for CO2 capture: A mini review
Peijing Shao, Jiexu Ye, Yao Shen, et al.
Gas Science and Engineering (2024) Vol. 123, pp. 205237-205237
Closed Access | Times Cited: 14
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: 12
Beam Search for Automated Design and Scoring of Novel ROR Ligands with Machine Intelligence**
Michaël Moret, Moritz Helmstädter, Francesca Grisoni, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 35, pp. 19477-19482
Open Access | Times Cited: 72
Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry
Pumidech Puthongkham, Supacha Wirojsaengthong, Akkapol Suea‐Ngam
The Analyst (2021) Vol. 146, Iss. 21, pp. 6351-6364
Closed Access | Times Cited: 71
Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine
Philippe Moingeon, Mélaine A. Kuenemann, Mickaël Guedj
Drug Discovery Today (2021) Vol. 27, Iss. 1, pp. 215-222
Closed Access | Times Cited: 65
Machine learning in bioprocess development: from promise to practice
Laura M. Helleckes, Johannes Hemmerich, Wolfgang Wiechert, et al.
Trends in biotechnology (2022) Vol. 41, Iss. 6, pp. 817-835
Open Access | Times Cited: 55
Chemical Space Exploration with Active Learning and Alchemical Free Energies
Yuriy Khalak, Gary Tresadern, David F. Hahn, et al.
Journal of Chemical Theory and Computation (2022) Vol. 18, Iss. 10, pp. 6259-6270
Open Access | Times Cited: 51
Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models
Tiago Janela, Jürgen Bajorath
Nature Machine Intelligence (2022) Vol. 4, Iss. 12, pp. 1246-1255
Closed Access | Times Cited: 35
Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration
Xiaochen Zhang, Chengkun Wu, Jiacai Yi, et al.
Research (2022) Vol. 2022
Open Access | Times Cited: 35
Distinct chemical environments in biomolecular condensates
Henry R. Kilgore, Peter G. Mikhael, Kalon J. Overholt, et al.
Nature Chemical Biology (2023) Vol. 20, Iss. 3, pp. 291-301
Closed Access | Times Cited: 33
Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Sukriti Singh, Raghavan B. Sunoj
Accounts of Chemical Research (2023) Vol. 56, Iss. 3, pp. 402-412
Closed Access | Times Cited: 28
Guided diffusion for inverse molecular design
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, et al.
Nature Computational Science (2023) Vol. 3, Iss. 10, pp. 873-882
Open Access | Times Cited: 27
Data-Driven Elucidation of Flavor Chemistry
Xingran Kou, Peiqin Shi, Chukun Gao, et al.
Journal of Agricultural and Food Chemistry (2023) Vol. 71, Iss. 18, pp. 6789-6802
Open Access | Times Cited: 26
De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime
Marco Ballarotto, Sabine Willems, Tanja Stiller, et al.
Journal of Medicinal Chemistry (2023) Vol. 66, Iss. 12, pp. 8170-8177
Open Access | Times Cited: 24
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Gary Tom, Riley J. Hickman, Aniket Zinzuwadia, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 759-774
Open Access | Times Cited: 23
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