Mainak Chatterjee

About

I am Dr Mainak Chatterjee, an Associate Professor of Bharat Technology—A School of Pharmacy in Uluberia, Howrah, West Bengal. I completed my PhD from Jadavpur University in 2024 under the supervision of Prof. Kunal Roy. I have prior experience in QSAR/q-RASAR model development, read-across prediction, and machine learning. My research interest lies mostly in computational modelling of ecotoxicological endpoints using various new approach methodologies.

Employment

• Bharat Technology Associate Professor
  2026 - Present
• Bharat Technology Assistant Professor
  2024 - 2026
• Jadavpur University Research Associate
  2023 - 2024

Education

• Jadavpur University PhD
  2019 - 2024
• National Institute of Pharmaceutical Education and Research M.S.Pharm
  2016 - 2018
• Maulana Abul Kalam Azad University of Technology, West Bengal B.Pharm
  2012 - 2016

Projects & Funding

Projects & funding information is unavailable.

Publications (16)

• Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches
  Environmental Science: Processes & Impacts 2024 DOI: 10.1039/d3em00445g
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• QSPR and q-RASPR predictions of the adsorption capacity of polyethylene, polypropylene and polystyrene microplastics for various organic pollutants in diverse aqueous environments
  Environmental Science: Nano 2024 DOI: 10.1039/D4EN00311J
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• Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species
  Aquatic Toxicology 2023 DOI: 10.1016/j.aquatox.2023.106776
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• Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees
  Journal of Hazardous Materials 2023 DOI: 10.1016/j.jhazmat.2023.132358
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• “Data fusion” quantitative read-across structure-activity-activity relationships (q-RASAARs) for the prediction of toxicities of binary and ternary antibiotic mixtures toward three bacterial species
  Journal of Hazardous Materials 2023 DOI: 10.1016/j.jhazmat.2023.132129
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• Predictive Quantitative Read-Across Structure–Property Relationship Modeling of the Retention Time (LogtR) of Pesticide Residues Present in Foods and Vegetables
  Journal of Agricultural and Food Chemistry 2023 DOI: 10.1021/acs.jafc.3c01438
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• Quantitative structure-activity relationships (QSARs) in medicinal chemistry
  Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development 2023 DOI: 10.1016/b978-0-443-18638-7.00029-3
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• Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri
  Chemosphere 2022 DOI: 10.1016/j.chemosphere.2022.136463
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• First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across
  Environmental Science and Pollution Research 2022 DOI: 10.1007/s11356-022-21937-w
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• Quantitative predictions from chemical read-across and their confidence measures
  Chemometrics and Intelligent Laboratory Systems 2022 DOI: 10.1016/j.chemolab.2022.104613
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