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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:

Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models
Arash Tayyebi, Ali Alshami, Zeinab Rabiei, et al.
Journal of Cheminformatics (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 21

Showing 21 citing articles:

Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan, et al.
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100049-100049
Open Access | Times Cited: 19
Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects
George Obaido, Ibomoiye Domor Mienye, Oluwaseun Francis Egbelowo, et al.
Machine Learning with Applications (2024) Vol. 17, pp. 100576-100576
Open Access | Times Cited: 15
Progress of machine learning in the application of small molecule druggability prediction
Junyao Li, Jianmei Zhang, Rui Guo, et al.
European Journal of Medicinal Chemistry (2025) Vol. 285, pp. 117269-117269
Closed Access | Times Cited: 1
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 33, pp. 23103-23120
Closed Access | Times Cited: 7
Machine learning – Driven surface grafting of thin-film composite reverse osmosis (TFC-RO) membrane
Arash Tayyebi, Ali Alshami, Erfan Tayyebi, et al.
Desalination (2024) Vol. 579, pp. 117502-117502
Closed Access | Times Cited: 5
Computational Methods for Predicting Chemical Reactivity of Covalent Compounds
Zhe Zhang, Ruyu Gao, Meiling Zhao, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Practical Machine Learning Model Selection and Interpretation for Organophosphorus Flame Retardancy in Epoxy Resin
Jiajun Li, Bin Zou, Amirbek Bekeshev, et al.
Polymer Degradation and Stability (2025) Vol. 234, pp. 111209-111209
Closed Access
Hybrid Semi-mechanistic and Machine Learning Solubility Regression Modeling for Crystallization Process Development
Gustavo Lunardon Quilló, Satyajeet Bhonsale, A. Collas, et al.
Crystal Growth & Design (2025)
Closed Access
A prospective on machine learning challenges, progress, and potential in polymer science
Daniel C. Struble, Bradley G. Lamb, Boran Ma
MRS Communications (2024) Vol. 14, Iss. 5, pp. 752-770
Open Access | Times Cited: 2
Molecular Fingerprints Optimization for Enhanced Predictive Modeling
Viktoriia Turkina, Melanie R.W. Messih, Etienne Kant, et al.
(2024)
Open Access | Times Cited: 1
Predicting small molecules solubility on endpoint devices using deep ensemble neural networks
Mayk Caldas Ramos, Andrew Dickson White
Digital Discovery (2024) Vol. 3, Iss. 4, pp. 786-795
Open Access | Times Cited: 1
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
(2024)
Open Access | Times Cited: 1
Studies for mutual solubility of TODGA and water at 298.15 K and 0.1 MPa
Shekhar Kumar
Journal of Radioanalytical and Nuclear Chemistry (2024) Vol. 333, Iss. 10, pp. 4995-5001
Open Access | Times Cited: 1
A Universal Framework for General Prediction of Physicochemical Properties: the Natural Growth Model
Jinming Fan, Chao Qian, Shaodong Zhou
Research (2024) Vol. 7
Open Access | Times Cited: 1
When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
(2024)
Open Access
Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks
Mayk Caldas Ramos, Andrew Dickson White
Digital Discovery (2024) Vol. 3, Iss. 5, pp. 1069-1070
Open Access
Avoiding Reward Hacking in Multi-Objective Molecular Design: A Data-Driven Generative Strategy with a Reliable Design Framework
Tatsuya Yoshizawa, Shoichi Ishida, Tomohiro Sato, et al.
(2024)
Open Access
GGAS2SN: Gated Graph and SmilesToSeq Network for Solubility Prediction
Waqar Ahmad, Kil To Chong, Hilal Tayara
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 20, pp. 7833-7843
Closed Access
Thermodynamic Assessment of the Pyrazinamide Dissolution Process in Some Organic Solvents
Jesus Tovar-Amézquita, Cristian Rincón-Guio, Francy Elaine Torres-Suarez, et al.
Molecules (2024) Vol. 29, Iss. 21, pp. 5089-5089
Open Access
Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models
Ulises Casado, Facundo I. Altuna, Luis A. Miccio
Sustainability (2024) Vol. 16, Iss. 23, pp. 10681-10681
Open Access
e-QSAR (Explainable AI-QSAR), molecular docking, and ADMET analysis of structurally diverse GSK3-beta modulators to identify concealed modulatory features vindicated by X-ray
Vijay H. Masand, Sami A. Al‐Hussain, Gaurav S. Masand, et al.
Computational Biology and Chemistry (2024) Vol. 115, pp. 108324-108324
Closed Access
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