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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 12 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
Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure–activity relationship (q-RASAR) with the application of machine learning
Arkaprava Banerjee, Supratik Kar, Kunal Roy, et al.
Critical Reviews in Toxicology (2024) Vol. 54, Iss. 9, pp. 659-684
Closed Access | Times Cited: 10
Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs
Arkaprava Banerjee, Kunal Roy
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure–activity relationship (q-RASAR) and pharmacophore modeling
Samima Khatun, Indrasis Dasgupta, Rakibul Islam, et al.
Molecular Diversity (2024)
Closed Access | Times Cited: 3
DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction
Tarapong Srisongkram
Computers in Biology and Medicine (2024) Vol. 178, pp. 108731-108731
Closed Access | Times Cited: 2
Machine learning-assisted c-RASAR modeling of a curated set of orally active nephrotoxic drugs: Similarity-based predictions from close source neighbors
Arkaprava Banerjee, Kunal Roy
(2024)
Open Access | Times Cited: 1
Accurate & simple open-sourced no-code machine learning and CDFT predictive models for the antioxidant activity of phenols
Andrés Halabi Diaz, Franco Galdames, Patricia Velásquez
Computational and Theoretical Chemistry (2024) Vol. 1239, pp. 114782-114782
Closed Access | Times Cited: 1
Tools, Applications, and Case Studies (q-RA and q-RASAR)
Kunal Roy, Arkaprava Banerjee
Springer briefs in molecular science (2024), pp. 51-88
Closed Access
Applying local interpretable model-agnostic explanations to identify substructures that are responsible for mutagenicity of chemical compounds
Lucca Caiaffa Santos Rosa, André Silva Pimentel
Molecular Systems Design & Engineering (2024) Vol. 9, Iss. 9, pp. 920-936
Closed Access
Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification
Indrasis Dasgupta, T Das, Biplab Das, et al.
Beilstein Journal of Nanotechnology (2024) Vol. 15, pp. 909-924
Open Access
Development of hybrid models by the integration of the Read-Across hypothesis with the QSAR framework for the assessment of Developmental and Reproductive Toxicity (DART) tested according to OECD TG 414
Sapna Kumari Pandey, Kunal Roy
Toxicology Reports (2024) Vol. 13, pp. 101822-101822
Open Access
Local QSAR based on quantum chemistry calculations for the stability of nitrenium ions to reduce false positive outcomes from standard QSAR systems for the mutagenicity of primary aromatic amines
Shigeharu Muto, A. Furuhama, Mika Yamamoto, et al.
Genes and Environment (2024) Vol. 46, Iss. 1
Open Access
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