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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:

Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach
Arkaprava Banerjee, Supratik Kar, Souvik Pore, et al.
Nanotoxicology (2023) Vol. 17, Iss. 1, pp. 78-93
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points
Arkaprava Banerjee, Kunal Roy
Chemical Research in Toxicology (2023) Vol. 36, Iss. 3, pp. 446-464
Closed Access | Times Cited: 46
Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure–Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients
Arkaprava Banerjee, Kunal Roy
Chemical Research in Toxicology (2023) Vol. 36, Iss. 9, pp. 1518-1531
Open Access | Times Cited: 40
From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization
Yutong Wang, Peng Wang, Tengjiao Fan, et al.
Journal of Hazardous Materials (2024) Vol. 476, pp. 134945-134945
Closed Access | Times Cited: 16
Integrating Machine Learning and Nano-QSAR Models to Predict the Oxidative Stress Potential Caused by Single and Mixed Carbon Nanomaterials in Algal Cells
Qi Qi, Zhuang Wang
Environmental Toxicology and Chemistry (2025)
Closed Access | Times Cited: 1
Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset
Arkaprava Banerjee, Kunal Roy
Chemometrics and Intelligent Laboratory Systems (2023) Vol. 237, pp. 104829-104829
Closed Access | Times Cited: 34
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
Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye‐sensitized solar cells (DSSCs)
Souvik Pore, Arkaprava Banerjee, Kunal Roy
Molecular Informatics (2024) Vol. 43, Iss. 4
Closed Access | Times Cited: 9
“Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species
Mainak Chatterjee, Kunal Roy
Journal of Hazardous Materials (2023) Vol. 459, pp. 132129-132129
Closed Access | Times Cited: 20
Machine learning-based q-RASPR modeling of power conversion efficiency of organic dyes in dye-sensitized solar cells
Souvik Pore, Arkaprava Banerjee, Kunal Roy
Sustainable Energy & Fuels (2023) Vol. 7, Iss. 14, pp. 3412-3431
Closed Access | Times Cited: 19
Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals
Arkaprava Banerjee, Kunal Roy
Environmental Science Processes & Impacts (2023) Vol. 25, Iss. 10, pp. 1626-1644
Closed Access | Times Cited: 16
Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint
Qi Qi, Zhuang Wang
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 17, pp. 25114-25128
Closed Access | Times Cited: 6
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
First report on q-RASTR modelling of hazardous dose (HD 5 ) for acute toxicity of pesticides: an efficient and reliable approach towards safeguarding the sensitive avian species
Saurabh Das, Arnab Bhattacharjee, Probir Kumar Ojha
SAR and QSAR in environmental research (2025), pp. 1-17
Closed Access
Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease
Vinay Kumar, Arkaprava Banerjee, Kunal Roy
Chemometrics and Intelligent Laboratory Systems (2023) Vol. 245, pp. 105049-105049
Closed Access | Times Cited: 14
Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective
Yunchi Zhou, Ying Wang, Willie J.G.M. Peijnenburg, et al.
Environmental Science & Technology (2024)
Closed Access | Times Cited: 4
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
How safe are wild-caught salmons exposed to various industrial chemicals? First ever in silico models for salmon toxicity data gaps filling
Siyun Yang, Supratik Kar
Journal of Hazardous Materials (2024) Vol. 477, pp. 135401-135401
Closed Access | Times Cited: 3
First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling
Siyun Yang, Supratik Kar
The Science of The Total Environment (2023) Vol. 907, pp. 167991-167991
Closed Access | Times Cited: 9
Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR
Andrea Gallagher, Supratik Kar
Chemosphere (2023) Vol. 349, pp. 140810-140810
Closed Access | Times Cited: 9
Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure–Activity Relationships (q-RASAR)—Genesis and Model Development
Kunal Roy, Arkaprava Banerjee
Springer briefs in molecular science (2024), pp. 31-49
Closed Access | Times Cited: 2
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: 1
QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments
Md Mobarak Hossain, Arkaprava Banerjee, Mainak Chatterjee, et al.
Environmental Science Nano (2024) Vol. 11, Iss. 10, pp. 4196-4210
Closed Access | Times Cited: 1
Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles
Supratik Kar, Siyun Yang
Beilstein Journal of Nanotechnology (2024) Vol. 15, pp. 1142-1152
Open Access | Times Cited: 1
Prediction of Dynamic Toxicity of Nanoparticles Using Machine Learning
Ivan Khokhlov, Leonid Legashev, Irina Bolodurina, et al.
Toxics (2024) Vol. 12, Iss. 10, pp. 750-750
Open Access | Times Cited: 1
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