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Souvik Pore

Showing results (1-10 of 11) with videos related to

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Computational and Structural Biotechnology Journal|July 28, 2025
"intelligent Read Across (iRA)"- A tool for read-across-based toxicity prediction of nanoparticlesSouvik Pore, Kunal Roy
European Journal of Medicinal Chemistry|June 9, 2026
OralAbsPredict: A data-driven framework to predict human intestinal absorption (HIA) and human oral bioavailability (HOB) from chemical structuresSouvik Pore, Kunal Roy
Beilstein Journal of Nanotechnology|September 22, 2023
Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO<sub>2</sub> with periodic table descriptors using machine learning approachesJoyita Roy, Souvik Pore, Kunal Roy
NAM Journal|June 29, 2026
A q-RASAR approach for oral and inhalational toxicity prediction of perfluorinated and polyfluorinated compounds (PFCs) using rodent toxicity dataSagnik Sarkar, Souvik Pore, Kunal Roy
Molecular Informatics|February 20, 2024
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)Souvik Pore, Arkaprava Banerjee, Kunal Roy
Chemical Research in Toxicology|March 25, 2026
Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following Organisation for Economic Co-operation and Development Test Guidelines 407, 408, and 422 Supported by Experimental ValidationSouvik Pore, Zsuzsanna Szepesi, Kunal Roy
The Science of the Total Environment|September 3, 2025
Assessing biodegradability potential of organic chemicals in aquatic and soil environment through classification-based machine learning models developed in accordance with OECD standardsShubham Kumar Pandey, Souvik Pore, Kunal Roy
The Science of the Total Environment|December 30, 2025
HydroFate - A machine learning-based classification modeling platform for the prediction of hydrolytic stability of organic chemicals across different pH environmentsShubham Kumar Pandey, Souvik Pore, Kunal Roy
Nanotoxicology|March 9, 2023
Efficient predictions of cytotoxicity of TiO<sub>2</sub>-based multi-component nanoparticles using a machine learning-based q-RASAR approachArkaprava Banerjee, Supratik Kar, Souvik Pore, 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.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Computational and Structural Biotechnology Journal|July 28, 2025
"intelligent Read Across (iRA)"- A tool for read-across-based toxicity prediction of nanoparticlesSouvik Pore, Kunal Roy
European Journal of Medicinal Chemistry|June 9, 2026
OralAbsPredict: A data-driven framework to predict human intestinal absorption (HIA) and human oral bioavailability (HOB) from chemical structuresSouvik Pore, Kunal Roy
Beilstein Journal of Nanotechnology|September 22, 2023
Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO<sub>2</sub> with periodic table descriptors using machine learning approachesJoyita Roy, Souvik Pore, Kunal Roy
NAM Journal|June 29, 2026
A q-RASAR approach for oral and inhalational toxicity prediction of perfluorinated and polyfluorinated compounds (PFCs) using rodent toxicity dataSagnik Sarkar, Souvik Pore, Kunal Roy
Molecular Informatics|February 20, 2024
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)Souvik Pore, Arkaprava Banerjee, Kunal Roy
Chemical Research in Toxicology|March 25, 2026
Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following Organisation for Economic Co-operation and Development Test Guidelines 407, 408, and 422 Supported by Experimental ValidationSouvik Pore, Zsuzsanna Szepesi, Kunal Roy
The Science of the Total Environment|September 3, 2025
Assessing biodegradability potential of organic chemicals in aquatic and soil environment through classification-based machine learning models developed in accordance with OECD standardsShubham Kumar Pandey, Souvik Pore, Kunal Roy
The Science of the Total Environment|December 30, 2025
HydroFate - A machine learning-based classification modeling platform for the prediction of hydrolytic stability of organic chemicals across different pH environmentsShubham Kumar Pandey, Souvik Pore, Kunal Roy
Nanotoxicology|March 9, 2023
Efficient predictions of cytotoxicity of TiO<sub>2</sub>-based multi-component nanoparticles using a machine learning-based q-RASAR approachArkaprava Banerjee, Supratik Kar, Souvik Pore, 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.
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