Roy K - Search Results

1

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.

Pandey SK, Roy K. Pandey SK, et al. Among authors: roy k. Toxicol Rep. 2024 Nov 19;13:101822. doi: 10.1016/j.toxrep.2024.101822. eCollection 2024 Dec. Toxicol Rep. 2024. PMID: 39649380 Free PMC article.

2

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests.

Das RN, Sintra TE, Coutinho JAP, Ventura SPM, Roy K, Popelier PLA. Das RN, et al. Among authors: roy k. Toxicol Res (Camb). 2016 Jul 7;5(5):1388-1399. doi: 10.1039/c6tx00180g. eCollection 2016 Sep 1. Toxicol Res (Camb). 2016. PMID: 30090443 Free PMC article.

3

Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs).

Pandey SK, Ojha PK, Roy K. Pandey SK, et al. Among authors: roy k. Chemosphere. 2020 Aug;252:126508. doi: 10.1016/j.chemosphere.2020.126508. Epub 2020 Mar 18. Chemosphere. 2020. PMID: 32240857

4

QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices.

Pandey SK, Roy K. Pandey SK, et al. Among authors: roy k. Ecotoxicol Environ Saf. 2021 Jan 15;208:111411. doi: 10.1016/j.ecoenv.2020.111411. Epub 2020 Oct 17. Ecotoxicol Environ Saf. 2021. PMID: 33080425 Free article.

5

Prediction reliability of QSAR models: an overview of various validation tools.

De P, Kar S, Ambure P, Roy K. De P, et al. Among authors: roy k. Arch Toxicol. 2022 May;96(5):1279-1295. doi: 10.1007/s00204-022-03252-y. Epub 2022 Mar 10. Arch Toxicol. 2022. PMID: 35267067 Review.

6

On Some Novel Similarity-Based Functions Used in the ML-Based q-RASAR Approach for Efficient Quantitative Predictions of Selected Toxicity End Points.

Banerjee A, Roy K. Banerjee A, et al. Among authors: roy k. Chem Res Toxicol. 2023 Mar 20;36(3):446-464. doi: 10.1021/acs.chemrestox.2c00374. Epub 2023 Feb 22. Chem Res Toxicol. 2023. PMID: 36811528

7

Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across.

Nath A, Ojha PK, Roy K. Nath A, et al. Among authors: roy k. Aquat Toxicol. 2023 Apr;257:106429. doi: 10.1016/j.aquatox.2023.106429. Epub 2023 Feb 25. Aquat Toxicol. 2023. PMID: 36842883

8

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.

Banerjee A, Roy K. Banerjee A, et al. Among authors: roy k. Chem Res Toxicol. 2023 Sep 18;36(9):1518-1531. doi: 10.1021/acs.chemrestox.3c00155. Epub 2023 Aug 16. Chem Res Toxicol. 2023. PMID: 37584642

9

Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems.

Pandey SK, Roy K. Pandey SK, et al. Among authors: roy k. Toxicology. 2023 Dec;500:153676. doi: 10.1016/j.tox.2023.153676. Epub 2023 Nov 21. Toxicology. 2023. PMID: 37993082

10

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species.

Ghosh S, Chatterjee M, Roy K. Ghosh S, et al. Among authors: roy k. Aquat Toxicol. 2023 Dec;265:106776. doi: 10.1016/j.aquatox.2023.106776. Epub 2023 Nov 20. Aquat Toxicol. 2023. PMID: 38006764

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