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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.

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Showing 14 citing articles:

How to correctly develop q-RASAR models for predictive cheminformatics
Arkaprava Banerjee, Kunal Roy
Expert Opinion on Drug Discovery (2024) Vol. 19, Iss. 9, pp. 1017-1022
Closed Access | Times Cited: 10
Introduction to Predicting Properties of Organic Materials
Didier Mathieu
Challenges and advances in computational chemistry and physics (2025), pp. 27-63
Closed Access | Times Cited: 1
Exploring QSTR and q-RASTR modeling of agrochemical toxicity on cabbage for environmental safety and human health
Surbhi Jyoti, Anjali Murmu, Balaji Wamanrao Matore, et al.
Environmental Science and Pollution Research (2025)
Closed Access
Machine Learning–Assisted Design of Ytterbium-Based Materials with Tunable Bandgaps and Enhanced Stability
Sajjad Hussain Sumrra, Mamduh J. Aljaafreh, Sadaf Noreen, et al.
Brazilian Journal of Physics (2025) Vol. 55, Iss. 3
Closed Access
Applications of Predictive Modeling for Dye-Sensitized Solar Cells (DSSCs)
Supratik Kar
Challenges and advances in computational chemistry and physics (2025), pp. 167-198
Closed Access
A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
Cihat Güleryüz, Sajjad Hussain Sumrra, Abrar U. Hassan, et al.
Materials Chemistry and Physics (2025), pp. 130786-130786
Closed Access
Machine Learning Tools and Web Services for Materials Science Modeling
Shahin Ahmadi, Azizeh Abdolmaleki, Fereshteh Shiri, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 253-282
Closed Access
Applications of Quantitative Read-Across Structure–Property Relationship (q-RASPR) Modeling in the Field of Materials Science
S. K. Pandey, Souvik Pore, Arkaprava Banerjee, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 167-190
Closed Access
The Round Robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential
Dimitra‐Danai Varsou, Arkaprava Banerjee, Joyita Roy, et al.
(2024)
Open Access | Times Cited: 3
The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential
Dimitra‐Danai Varsou, Arkaprava Banerjee, Joyita Roy, et al.
Beilstein Journal of Nanotechnology (2024) Vol. 15, pp. 1536-1553
Open Access | Times Cited: 2
Innovative computational techniques for DSSCs using machine learning: a review
V S Yadav, Rahul Bhatnagar, Upendra Kumar
Deleted Journal (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 1
Predictive cheminformatics modeling of reorganization energy (RE) for p-type organic semiconductors: Integration of quantitative read-across structure-property relationship (q-RASPR) and stacking regression analysis
Shubham Kumar Pandey, Kunal Roy
Materials Today Communications (2024) Vol. 41, pp. 110430-110430
Closed Access | Times Cited: 1
ML‐based Read‐Across Structure‐Property Relationship (RASPR) strategy for Predicting Protein Resistance of Self-Assembled Monolayers (SAMs) as anti-biofouling materials
Indrasis Dasgupta, Biplab Das, Sk. Abdul Amin, et al.
Materials Today Communications (2024), pp. 111089-111089
Closed Access | Times Cited: 1
Intelligent Consensus-Based Predictions of Early Life Stage Toxicity in Fish Tested in Compliance with OECD Test Guideline 210
Souvik Pore, A. Pelloux, Anders Bergqvist, et al.
Aquatic Toxicology (2024) Vol. 279, pp. 107216-107216
Closed Access
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