Roy K - Search Results

1

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

2

Development of a robust and validated 2D-QSPR model for sweetness potency of diverse functional organic molecules.

Ojha PK, Roy K. Ojha PK, et al. Among authors: roy k. Food Chem Toxicol. 2018 Feb;112:551-562. doi: 10.1016/j.fct.2017.03.043. Epub 2017 Mar 24. Food Chem Toxicol. 2018. PMID: 28344088

3

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.

4

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.

5

First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability.

Banerjee A, Roy K. Banerjee A, et al. Among authors: roy k. Mol Divers. 2022 Oct;26(5):2847-2862. doi: 10.1007/s11030-022-10478-6. Epub 2022 Jun 29. Mol Divers. 2022. PMID: 35767129

6

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across.

Banerjee A, De P, Kumar V, Kar S, Roy K. Banerjee A, et al. Among authors: roy k. Chemosphere. 2022 Dec;309(Pt 1):136579. doi: 10.1016/j.chemosphere.2022.136579. Epub 2022 Sep 26. Chemosphere. 2022. PMID: 36174732

7

A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites.

Banerjee A, Gajewicz-Skretna A, Roy K. Banerjee A, et al. Among authors: roy k. Mol Inform. 2023 Apr;42(4):e2200261. doi: 10.1002/minf.202200261. Epub 2023 Feb 14. Mol Inform. 2023. PMID: 36618002

8

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

9

Efficient predictions of cytotoxicity of TiO₂-based multi-component nanoparticles using a machine learning-based q-RASAR approach.

Banerjee A, Kar S, Pore S, Roy K. Banerjee A, et al. Among authors: roy k. Nanotoxicology. 2023 Feb;17(1):78-93. doi: 10.1080/17435390.2023.2186280. Epub 2023 Mar 8. Nanotoxicology. 2023. PMID: 36891579

10

Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees.

Chatterjee M, Banerjee A, Tosi S, Carnesecchi E, Benfenati E, Roy K. Chatterjee M, et al. Among authors: roy k. J Hazard Mater. 2023 Oct 15;460:132358. doi: 10.1016/j.jhazmat.2023.132358. Epub 2023 Aug 22. J Hazard Mater. 2023. PMID: 37634379

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