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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:

Explaining molecular properties with natural language
Heta A. Gandhi, Andrew Dickson White
(2022)
Open Access | Times Cited: 21

Showing 21 citing articles:

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking
Zhenhua Wu, Jike Wang, Hongyan Du, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 64
The future of chemistry is language
Andrew Dickson White
Nature Reviews Chemistry (2023) Vol. 7, Iss. 7, pp. 457-458
Closed Access | Times Cited: 48
A Perspective on Explanations of Molecular Prediction Models
Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 8, pp. 2149-2160
Open Access | Times Cited: 46
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: 15
Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining
Miguel Mateu‐Sanz, Carla V. Fuenteslópez, Juan Manuel Uribe, et al.
Trends in biotechnology (2023) Vol. 42, Iss. 4, pp. 402-417
Open Access | Times Cited: 23
Identifying Substructures That Facilitate Compounds to Penetrate the Blood–Brain Barrier via Passive Transport Using Machine Learning Explainer Models
Lucca Caiaffa Santos Rosa, Caio Oliveira Argolo, Cayque Monteiro Castro Nascimento, et al.
ACS Chemical Neuroscience (2024) Vol. 15, Iss. 11, pp. 2144-2159
Open Access | Times Cited: 7
Prompt engineering of GPT-4 for chemical research: what can/cannot be done?
Kan Hatakeyama‐Sato, Naoki Yamane, Yasuhiko Igarashi, et al.
(2023)
Open Access | Times Cited: 15
Prompt engineering of GPT-4 for chemical research: what can/cannot be done?
Kan Hatakeyama‐Sato, Naoki Yamane, Yasuhiko Igarashi, et al.
Science and Technology of Advanced Materials Methods (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 15
Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models
Geemi P. Wellawatte, Philippe Schwaller
Communications Chemistry (2025) Vol. 8, Iss. 1
Open Access
A Review of Large Language Models and Autonomous Agents in Chemistry
Mayk Caldas Ramos, Christopher J. Collison, Andrew Dickson White
Chemical Science (2024)
Open Access | Times Cited: 4
Machine learning-based discovery of molecular descriptors that control polymer gas permeation
Tejus Shastry, Yasemin Basdogan, Zhen‐Gang Wang, et al.
Journal of Membrane Science (2024) Vol. 697, pp. 122563-122563
Closed Access | Times Cited: 3
From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery
Zhenxing Wu, Jihong Chen, Yitong Li, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 24, pp. 7617-7627
Closed Access | Times Cited: 8
De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning
Gavin Ye
Journal of Computer-Aided Molecular Design (2024) Vol. 38, Iss. 1
Open Access | Times Cited: 2
Explaining compound activity predictions with a substructure-aware loss for graph neural networks
Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Explainable AI methodology for understanding fault detection results during Multi-Mode operations
Abhijit Bhakte, Piyush Kumar Kumawat, Rajagopalan Srinivasan
Chemical Engineering Science (2024) Vol. 299, pp. 120493-120493
Closed Access | Times Cited: 1
Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature
Chunwei Ma, Russell D. Wolfinger
Chemical Research in Toxicology (2024) Vol. 37, Iss. 9, pp. 1524-1534
Closed Access
Explaining compound activity predictions with a substructure-aware loss for graph neural networks
Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna
(2023)
Open Access | Times Cited: 1
Machine Learning-Based Discovery of Molecular Descriptors that Control Polymer Gas Permeation
Tejus Shastry, Yasemin Basdogan, Zhen‐Gang Wang, et al.
(2023)
Closed Access | Times Cited: 1
Ground truth explanation dataset for chemical property prediction on molecular graphs
Eugen Hruška, Liang Zhao, Fang Liu
(2022)
Open Access | Times Cited: 1
Ground truth explanation dataset for chemical property prediction on molecular graphs
Eugen Hruška, Liang Zhao, Fang Liu
(2022)
Open Access | Times Cited: 1
Explaining compound activity predictions with a substructure-aware loss for graph neural networks
Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna
(2023)
Open Access
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