Banerjee A - Search Results

1

The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset.

Banerjee A, Roy K. Banerjee A, et al. Sci Rep. 2024 Sep 6;14(1):20812. doi: 10.1038/s41598-024-71892-4. Sci Rep. 2024. PMID: 39242880 Free PMC article.

2

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

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

Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and fi …

Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of …

3

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. Chemosphere. 2022 Dec;309(Pt 1):136579. doi: 10.1016/j.chemosphere.2022.136579. Epub 2022 Sep 26. Chemosphere. 2022. PMID: 36174732

4

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. Mol Inform. 2023 Apr;42(4):e2200261. doi: 10.1002/minf.202200261. Epub 2023 Feb 14. Mol Inform. 2023. PMID: 36618002

5

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. 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

6

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. Nanotoxicology. 2023 Feb;17(1):78-93. doi: 10.1080/17435390.2023.2186280. Epub 2023 Mar 8. Nanotoxicology. 2023. PMID: 36891579

7

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. 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

8

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: banerjee a. 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

9

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.

Banerjee A, Roy K. Banerjee A, et al. Environ Sci Process Impacts. 2023 Oct 18;25(10):1626-1644. doi: 10.1039/d3em00322a. Environ Sci Process Impacts. 2023. PMID: 37682520

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).

Pore S, Banerjee A, Roy K. Pore S, et al. Among authors: banerjee a. Mol Inform. 2024 Apr;43(4):e202300210. doi: 10.1002/minf.202300210. Epub 2024 Feb 19. Mol Inform. 2024. PMID: 38374528

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