Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Showing 1-25 of 38 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: 48
ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data
Arkaprava Banerjee, Kunal Roy
Environmental Science Processes & Impacts (2024) Vol. 26, Iss. 6, pp. 991-1007
Open Access | Times Cited: 28
Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction
Vinay Kumar, Arkaprava Banerjee, Kunal Roy
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 10, pp. 4298-4309
Closed Access | Times Cited: 17
Quantitative predictions from chemical read-across and their confidence measures
Arkaprava Banerjee, Mainak Chatterjee, Priyanka De, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 227, pp. 104613-104613
Open Access | Times Cited: 54
Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds
Muhammad Ishfaq, Muhammad Aamir, Farooq Ahmad, et al.
ACS Omega (2022) Vol. 7, Iss. 51, pp. 48139-48149
Open Access | Times Cited: 43
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: 41
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: 36
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
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: 12
QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency
Guohui Sun, Peiying Bai, Tengjiao Fan, et al.
Pharmaceutics (2023) Vol. 15, Iss. 8, pp. 2170-2170
Open Access | Times Cited: 19
Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats
Shilpayan Ghosh, Kunal Roy
Toxicology (2024) Vol. 505, pp. 153824-153824
Closed Access | Times Cited: 7
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: 6
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
Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT
Balaji Wamanrao Matore, Partha Pratim Roy, Jagadish Singh
Journal of Biomolecular Structure and Dynamics (2023) Vol. 41, Iss. 22, pp. 13056-13077
Closed Access | Times Cited: 13
The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods
Xinyi Lu, Xin Wang, Shuo Chen, et al.
Archives of Toxicology (2024) Vol. 98, Iss. 7, pp. 2213-2229
Closed Access | Times Cited: 5
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: 12
Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents
Anguraj Moulishankar, Sundarrajan Thirugnanasambandam
Journal of Receptors and Signal Transduction (2023) Vol. 43, Iss. 3, pp. 83-92
Closed Access | Times Cited: 11
Comprehensive ecotoxicological assessment of pesticides on multiple avian species: Employing quantitative structure-toxicity relationship (QSTR) modeling and read-across
Shubha Das, Abhisek Samal, Ankur Kumar, et al.
Process Safety and Environmental Protection (2024) Vol. 188, pp. 39-52
Closed Access | Times Cited: 4
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
ARKA: A framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data
Arkaprava Banerjee, Kunal Roy
(2024)
Open Access | Times Cited: 3
Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies
Surbhi Goyal, Payal Rani, Monika Chahar, et al.
Journal of Biomolecular Structure and Dynamics (2023) Vol. 41, Iss. 23, pp. 13616-13631
Closed Access | Times Cited: 8
Modeling and insights into the structural characteristics of endocrine-disrupting chemicals
Ruiqiu Zhang, Bailun Wang, Ling Li, et al.
Ecotoxicology and Environmental Safety (2023) Vol. 263, pp. 115251-115251
Open Access | Times Cited: 7
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
Chemical-induced liver cancer: an adverse outcome pathway perspective
Julen Sanz-Serrano, Ellen Callewaert, Sybren De Boever, et al.
Expert Opinion on Drug Safety (2024) Vol. 23, Iss. 4, pp. 425-438
Closed Access | Times Cited: 2
Synthetic Endocrine Disruptors in Fragranced Products
S. J. H. Ashcroft, Noura S. Dosoky, William N. Setzer, et al.
Endocrines (2024) Vol. 5, Iss. 3, pp. 366-381
Open Access | Times Cited: 2
Page 1 - Next Page

Scroll to top