List of Publications

[Dr. Kunal Roy, M. Pharm, Ph.D., Drug Theoretics & Cheminformatics Laboratory,

Dept. Pharmaceutical Technology, Jadavpur University.

E-mail : kunalroy_in@yahoo.com]

[Only published, in print and accepted papers are listed]

(International publications are marked with *)

[Recipient of Bioorganic and Medicinal Chemistry Most Cited Paper 2003-2006, 2004-2007 and 2006-2009 Awards from Elsevier, The Netherlands]

[Recipient of Bioorganic and Medicinal Chemistry Letters Most Cited Paper 2006-2009 Award from Elsevier, The Netherlands]

Fields of Research Interest

1.                 Exploring QSARs of Ligands acting on Pharmacologically Relevant Targets of Contemporary Interest

2.         Exploring QSARs of Antioxidants

3.         Modeling of Physicochemical Properties of Organic Compounds

4.         Exploring QSTRs of Chemicals and Environmental Pollutants

5.         Modeling of Agrochemicals (insecticides, herbicides, plant growth regulators)

6.                 Exploring QSAR/QSPR/QSTR with Novel Extended Topochemical Atom (ETA) Indices developed in the Drug Theoretics Lab

Keywords: Chemometrics, QSAR, QSPR, QSTR.

ResearcherID: B-1673-2009

ResearchGate: http://www.researchgate.net/profile/Kunal_Roy2/publications/

SCOPUS Author ID: 56962764800

Pubmed:http://www.ncbi.nlm.nih.gov/pubmed?term=(roy%20k%5BAuthor%5D)%20AND%20Jadavpur%5BAffiliation%5D

Google scholar citation page: http://scholar.google.com/citations?user=j5iRuhwAAAAJ&hl=en

ORCID: http://orcid.org/0000-0003-4486-8074

Research area of interest: QSAR and Molecular Modeling with application in Drug Design, Predictive Toxicology and Materials Science

Placed in the list of the World's Top 2% science-wide author database (2022) (World rank 56 in the subfield of Medicinal & Biomolecular Chemistry) (Ioannidis, John P.A. (2023), "October 2023 data-update for Updated science-wide author databases of standardized citation indicators", Elsevier Data Repository, V6, doi: 10.17632/btchxktzyw.6). 

Recent Important Publications

• Banerjee A, Roy K, 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. Environ. Sci.: Processes Impacts, 25, 2023, 1626–1644, https://doi.org/10.1039/D3EM00322A 

• Chatterjee M, Banerjee A, Tosi S, Carnesecchi E, Benfenati E, Roy K, Machine learning - based q-RASAR modeling to predict acute contact toxicity of binary organic pesticide mixtures in honey bees. J Hazard Mater, 2023, https://doi.org/10.1016/j.jhazmat.2023.132358 

• Banerjee A, Roy K, 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. Chem Res Toxicol, 2023, https://doi.org/10.1021/acs.chemrestox.3c00155 

• Pore S, Banerjee A, Roy K, Machine Learning - Based q-RASPR Modeling of Power Conversion Efficiency of Organic Dyes in Dye-Sensitized Solar Cells. Sustainable Energy and Fuels, 2023, https://doi.org/10.1039/D3SE00457K 

• Ghosh S, Chatterjee M, Roy K, Predictive Quantitative Read-Across Structure–Property Relationship Modeling of the Retention Time (Log tR) of Pesticide Residues Present in Foods and Vegetables. J Agric Food Chem 2023, https://doi.org/10.1021/acs.jafc.3c01438

• Banerjee A, Roy K, Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset. Chemom Intell Lab Syst, 2023, https://doi.org/10.1016/j.chemolab.2023.104829 

• Banerjee A, Roy K, On some novel similarity-based functions used in the ML-based q-RASAR approach for efficient quantitative predictions of selected toxicity endpoints. Chem Res Toxicol, 36, 2023, 446-464,  https://doi.org/10.1021/acs.chemrestox.2c00374 

• Khan K, Kumar V, Colombo E, Lombardo A, Benfenati E, Roy K, Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragment-based descriptors. Environ Int, 170, 2022, 107625, https://doi.org/10.1016/j.envint.2022.107625 

• Banerjee A, De P, Kumar V, Kar S, Roy K, Quick and Efficient Quantitative Predictions of Androgen Receptor Binding Affinity for Screening Endocrine Disruptor Chemicals Using 2D-QSAR and Chemical Read-Across. Chemosphere, 309, 2022, no.  136579, https://doi.org/10.1016/j.chemosphere.2022.136579    

• Chatterjee M, Roy K, 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 308, 2022, no. 136463, https://doi.org/10.1016/j.chemosphere.2022.136463  

• Banerjee A, Chatterjee M, De P, Roy K, Quantitative Predictions from Chemical Read-Across and Their Confidence Measures. Chemom Intell Lab Syst 227C, 2022, no.  104613, https://doi.org/10.1016/j.chemolab.2022.104613   

• Banerjee A, Roy K, First report of q-RASAR modeling towards an approach of easy interpretability and efficient transferability. Mol Divers 2022, http://dx.doi.org/10.1007/s11030-022-10478-6 

• Chatterjee M, Roy K, Recent Advances on Modelling the Toxicity of Environmental Pollutants for Risk Assessment: from Single Pollutants to Mixtures. Curr Pollut Rep, 2022, https://doi.org/10.1007/s40726-022-00219-6 

• De P, Kar S, Ambure P, Roy K, Prediction reliability of QSAR models: an overview of various validation tools. Archiv Toxicol, 2022, https://doi.org/10.1007/s00204-022-03252-y 

• Khan K, Roy K, Ecotoxicological risk assessment of organic compounds against various aquatic and terrestrial species: application of interspecies i-QSTTR and species sensitivity distribution techniques. Green Chem 24, 2022, 2160-2178, https://doi.org/10.1039/D1GC04320J 

• Kar S, Sanderson H, Roy K, Benfenati E, Leszczynski J, Green Chemistry in Synthesis of Pharmaceuticals, Chemical Reviews, 2021, https://doi.org/10.1021/acs.chemrev.1c00631  

• Mukherjee R K, Kumar V, Roy K, Ecotoxicological QSTR and QSTTR modeling for the prediction of acute oral toxicity of pesticides against multiple avian species, Environmental Science and Technology, 2021, https://doi.org/10.1021/acs.est.1c05732 

• Lavado GJ Baderna D, Gadaleta D, Ultre M, Roy K, Benfenati E, Ecotoxicological QSAR modeling of the acute toxicity of organic compounds to the freshwater crustacean Thamnocephalus platyurus. Chemosphere, 280, 2021, 130652, https://doi.org/10.1016/j.chemosphere.2021.130652 

• Chatterjee M, Roy K, Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors. Journal of Hazardous Materials, 408, 2021, 124936, https://doi.org/10.1016/j.jhazmat.2020.124936 

• Seth A, Roy K, QSAR modeling of algal low level toxicity values of different phenol and aniline derivatives using 2D descriptors.

Aquatic Toxicology, 228, 2020, article 105627, http://dx.doi.org/10.1016/j.aquatox.2020.105627 

• Pandey S, Roy K, QSPR modeling of octanol-water partition coefficient and organic carbon normalized sorption coefficient of diverse organic chemicals using Extended Topochemical Atom (ETA) indices. Ecotoxicology and Environmental Safety, 208, 2021, article 111411, http://dx.doi.org/10.1016/j.ecoenv.2020.111411 

• Seth A, Ojha P, Roy K, QSAR modeling with ETA indices for cytotoxicity and enzymatic activity of diverse chemicals. Journal of Hazardous Materials, 394, 2020, article 122498, http://dx.doi.org/10.1016/j.jhazmat.2020.122498

• Pandey S, Ojha P, Roy K, Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs). Chemosphere, 252, 2020, article 126508, http://dx.doi.org/10.1016/j.chemosphere.2020.126508

• Ojha PK, Kar S, Krishna JG, Roy K, Leszczynski, Therapeutics for COVID-19: from computation to practices—where we are, where we are heading to. Mol Divers 25, 2021, 625-659, http://dx.doi.org/10.1007/s11030-020-10134-x

• Kumar V, Roy K, Development of a simple, interpretable and easily transferable QSAR model for quick screening antiviral databases in search of novel 3C-like protease (3CLpro) enzyme inhibitors against SARS-CoV diseases. SAR QSAR Environ Res, 31, 2020, 511-526, https://doi.org/10.1080/1062936X.2020.1776388