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Arkaprava Banerjee

Showing results (21-30 of 28) with videos related to

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Chemosphere|September 29, 2022
Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-AcrossArkaprava Banerjee, Priyanka De, Vinay Kumar, et al.
Journal of Hazardous Materials|September 7, 2024
Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305Souvik Pore, Alexia Pelloux, Mainak Chatterjee, et al.
Scientific Reports|January 8, 2025
The q-RASPR approach for predicting the property and fate of persistent organic pollutantsChia Ming Chang, Arkaprava Banerjee, Vinay Kumar, et al.
Bioinformation|October 31, 2025
Correlation between clinical and CBCT-based crestal soft tissue and bone parameters in posterior edentulous mandibleAjit Mishra, Arkaprava Banerjee, Savita Ghom, et al.
Journal of Hazardous Materials|August 27, 2023
Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey beesMainak Chatterjee, Arkaprava Banerjee, Simone Tosi, et al.
Critical Reviews in Toxicology|September 3, 2024
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 learningArkaprava Banerjee, Supratik Kar, Kunal Roy, et al.
Beilstein Journal of Nanotechnology|December 3, 2024
The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potentialDimitra-Danai Varsou, Arkaprava Banerjee, Joyita Roy, et al.
Journal of Computer-Aided Molecular Design|June 8, 2026
A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning modelsArkaprava Banerjee, Vinay Kumar, Shubha Das, et al.
Pageof 3

Showing results (21-30 of 28) with videos related to

Sort By:
Pageof 3
You have reached the last page of results.This site can display upto 28 results.
Chemosphere|September 29, 2022
Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-AcrossArkaprava Banerjee, Priyanka De, Vinay Kumar, et al.
Journal of Hazardous Materials|September 7, 2024
Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305Souvik Pore, Alexia Pelloux, Mainak Chatterjee, et al.
Scientific Reports|January 8, 2025
The q-RASPR approach for predicting the property and fate of persistent organic pollutantsChia Ming Chang, Arkaprava Banerjee, Vinay Kumar, et al.
Bioinformation|October 31, 2025
Correlation between clinical and CBCT-based crestal soft tissue and bone parameters in posterior edentulous mandibleAjit Mishra, Arkaprava Banerjee, Savita Ghom, et al.
Journal of Hazardous Materials|August 27, 2023
Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey beesMainak Chatterjee, Arkaprava Banerjee, Simone Tosi, et al.
Critical Reviews in Toxicology|September 3, 2024
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 learningArkaprava Banerjee, Supratik Kar, Kunal Roy, et al.
Beilstein Journal of Nanotechnology|December 3, 2024
The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potentialDimitra-Danai Varsou, Arkaprava Banerjee, Joyita Roy, et al.
Journal of Computer-Aided Molecular Design|June 8, 2026
A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning modelsArkaprava Banerjee, Vinay Kumar, Shubha Das, et al.
Pageof 3