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Dr. Supratik Kar
Tenure-track Assistant Professor @Dept. of Chemistry, Kean University, NJ, USA
&
PI and Group Leader of
Chemometrics and Molecular
Modeling (CMM) Laboratory
4 Books, 23 Book chapter, 86 research and review articles
Text Books on QSAR, CADD and Solar Cell
3. K. Roy, S. Kar, R. N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press (Elsevier), 2015, Link
2. K. Roy, S. Kar, R. N. Das, A Primer on QSAR/QSPR Modeling, Springer, 2015, Link
1. J. K. Roy, S. Kar, J. Leszczynski, Development of Solar Cells – Theory and Experiment. Springer (2020). Link
Research and Review Articles
This section consists of Research, review, editorial, commentary articles covering CADD, Toxicity evaluation of Pharmaceuticals and Chemicals, Application of Cheminformatics in the field of Nanomaterials, Solar Cell design and development, Toxicity evaluation of mixtures, Risk assessment of endocrine disruptors, and development of new validation tools and metrics for QSAR
77. V. Kumar, S. Kar, P. De, K. Roy, J. Leszczynski, Identification of Potential Antivirals against 3CLpro Enzyme for the Treatment of SARS-CoV-2: A Multi-step Virtual Screening Study. SAR and QSAR in Environmental Research, 2022, https://doi.org/10.1080/1062936X.2022.2055140
76. P. De, S. Kar, P. Ambure, K. Roy, Prediction Reliability of QSAR Models: An Overview of Various Validation Tools. Archives of Toxicology, 2022, doi: 10.1007/s00204-022-03252-y
75. S. Kar, J. Leszczynski, Computational Approaches in Assessments of Mixture Toxicity. Current Opinion in Toxicology, 2022, 29, 31-35 Link
74. Supratik Kar, Hans Sanderson, Kunal Roy, Emilio Benfenati and Jerzy Leszczynski. Green Chemistry in the Synthesis of Pharmaceuticals. Chemical Reviews. 2021, https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00631
73. A. Gajewicz-Skretna, S. Kar, M. Piotrowska, J. Leszczynski, Is the Kernel-weighted local polynomial regression (KwLPR) approach choice over the traditional regression tools to develop QSAAR models? Journal of Cheminformatics, 2021, 13, 9. Link
72. S. Kar, K. Pathakoti, P.B. Tchounwou, D. Leszczynska, J. Leszczynski, Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies. Chemosphere, 2021, 264, 128428, Link
71. K. Kapusta, S. Kar, JT Collins, LM Franklin, W Kolodziejczyk, J Leszczynski, GA Hill. Protein Reliability Analysis and Virtual Screening of Natural Inhibitors for SARS-CoV-2 Main Protease (Mpro) Through Docking, Molecular Mechanic & Dynamic, and ADMET Profiling. Journal of Biomolecular Structure and Dynamics, 2020. Link
70. R. Ghosh, A. R. Cingreddy, V. Melapu, S. Joginipelli, S. Kar. Application of Artificial Intelligence and Machine Learning Techniques in Classifying Extent of Dementia Across Alzheimer's Image Data. International Journal of Quantitative Structure-Property Relationships, 2021, 6(2), 29-46. Link
69. A. R. Cingireddy, R. Ghosh, S. Kar, V. Melapu, S. Joginipeli, J. Leszczynski. Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile. International Journal of Quantitative Structure-Property Relationships, 2021, 6(3), 35-47. Link
68. S.S. Chitikina, P. Buddiga, P.K. Deb, R. P. Mailavaram, K. N. Venugopala, A. B. Nair, B. Al-Jaidi, S. Kar, Synthesis and anthelmintic activity of some novel (E)-2-methyl/propyl-4-(2-(substitutedbenzylidene)hydrazinyl)-5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidines. Med Chem Res, 2020, 29, 1600–1610. Link
67. P. K. Ojha, S. Kar, J. G. Krishna, K. Roy, J. Leszczynski, Therapeutics for COVID-19: From Computation to Practices -Where We are, Where We are Heading to, Molecular Diversity, 2021, 25, 625-659. Link
66. S. Kar, J. Leszczynski, From animal to human– interspecies analysis provides novel way of ascertaining and fighting COVID-19, The Innovation (Cell Press), 2020, 1, 100021. Link
65. S. Kar, J. Leszczynski, Open Access In Silico Tools to Predict the ADMET Profiling of Drug Candidates, Expert Opinion on Drug Discovery, 2020, Link
64. J. K. Roy, S. Kar, J. Leszczynski, Revealing the Photophysical Mechanism of N,N′-dialkyl/phenyl-aniline based Sensitizers with D–D–𝜋–A Framework: Theoretical Insights, ACS Sustainable Chemistry & Engineering, 2020, 8, 35, 13328–13341. Link
63. S. Kar, J. Leszczynski, Is Intraspecies QSTR Model Answer to Toxicity Data Gap Filling: Ecotoxicity Modeling of Chemicals to Avian Species, Science of the Total Environment, 2020, 738, 139858. Link
62. V. Uppar, S. Chandrashekharappa, K. N. Venugopala, P. K. Deb, S. Kar, O. I. Alwassil, R. M. Gleiser, D. Garcia, B. Odhav, M. K. Mohan, R. Venugopala, B. Padmashali. Synthesis and characterization of pyrrolo[1,2-a]quinoline derivatives for their larvicidal activity against Anopheles arabiensis, Structural Chemistry, 2020, 31, 1533–1543. Link
61. J. G. Krishna, P. K. Ojha, S. Kar, K. Roy, J. Leszczynski, Chemometric modeling of power conversion efficiency of organic dyes in dye sensitized solar cells for the future renewable energy, Nano Energy, 2020, 70, 104537. Link
60. S. Kar, H. Sanderson, K. Roy, E. Benfenati, J. Leszczynski, Ecotoxicological assessment of pharmaceuticals and personal care products using predictive toxicology approaches, Green Chemistry, 2020, 22, 1458-1516. Link
59. M. Khan, S. Kar, J. Wang, J. Leszczynski, Theoretical Study of Formate, Tartrate, Tartronate and Glycolate Production from 6-Carbon Trioxylate Intermediate in the Citric Acid Cycle, Journal of Molecular Modeling, 2019, 25, 347. Link
58. S. Ghosh, S. Kar, J. Leszczynski, Chemometric Modeling of Ecotoxicity of Endocrine Disruptors to an Avian Species Anas platyrhynchos. International Journal of Quantitative Structure-Property Relationships, 2020, 5(2), 1-16. Link
57. J. K. Roy, S. Kar*, J. Leszczynski*, Optoelectronic Properties of C60 and C70 Fullerene Derivatives: Designing and Evaluating Novel Candidates for Efficient P3HT Polymer Solar Cells, Materials, 2019, 12, 2282; Link
56. R. P. Mailavaram, O. H. Ahmed Al-Attraqchi, S. Kar*, S. Ghosh, Current Status in the Design and Development of Agonists and Antagonists of Adenosine A3 Receptor as Potential Therapeutic Agents. Current Pharmaceutical Design, 2019, 25(25), 2772-2787. Link
55. G. Hoover, S. Kar, S. Guffey, J. Leszczynski, Maria S. Sepúlveda, In vitro and in silico modeling of perfluoroalkyl substances mixture toxicity in an amphibian fibroblast cell line. Chemosphere, 2019, 233, 25-33. Link
54. P. N. Samanta, S. Kar, J. Leszczynski, Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors, Current Pharmaceutical Design, 2019, 25(7), 750-773. Link
53. S. Kar, J. Leszczynski, Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures, Toxics, 2019, 7, 15. Link
52. J. K. Roy, S. Kar, J. Leszczynski, Electronic Structure and Optical Properties of Designed Photo Efficient Indoline Based Dye-sensitizers with D-A-π-A Framework. Journal of Physical Chemistry C, 2019, 123, 3309−3320, Link
51. S. Kar, S. Ghosh, J. Leszczynski, Is clay-Polycation Adsorbent Future of the Greener Society? In Silico Modeling Approach with Comprehensive Virtual Screening. Chemosphere, 2019, 220, 1108-1117. Link
50. J. Jean, S. Kar, J. Leszczynski, Risk assessment of environmental contaminants through QSAR modeling of important PBTK parameter: A data gap filling approach, Environment International, 2018, 121, 1193-1203. Link
49. P. De, S. Kar, K. Roy, J. Leszczynski, Second Generation Periodic Table Based Descriptors to Encode Toxicity of Metal Oxide Nanoparticles to Multiple Species: QSTR Modeling for Exploration of Toxicity Mechanisms. Environmental Science: Nano, 2018, 5, 2742-2760. Link
48. K. Roy, P. Ambure, S. Kar, How precise are our QSAR derived predictions for new query chemicals?. ACS Omega, ACS Omega, 2018, 3 (9), pp 11392-11406. Link
47. K. Khan, S. Kar, H. Sanderson, K. Roy, J. Leszczynski, Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i-QSTTR Approaches: Application of 2D and Fragment Based Descriptors. Molecular Informatics. 2018, 37, 1800078, Link
46. P. K. Ojha, S. Kar, K. Roy, J. Leszczynski, Towards Comprehension of Multiple Human Cells Uptake of Engineered Nano Metal Oxides: Quantitative Inter Cell Line Uptake Specificity (QICLUS) modeling. Nanotoxicology, 2019,13, 14-34. Link
45. S. Kar, S. Ghosh, J. Leszczynski, Single or Mixture Halogenated Chemicals? Risk Assessment and Developmental Toxicity Prediction on Zebrafish Embryos Based on Weighted Descriptors Approach. Chemosphere, 2018, 210, 588-596. Link
44. J. K. Roy, S. Kar, J. Leszczynski, Insight into the optoelectronic properties of designed efficient tetrahydroquinoline dye-sensitizers on TiO2(101) surface for solar cells: first principles approach. Nature Scientific Reports, 2018, 8: 10997. Link
43. K. Roy, P. Ambure, S. Kar, P. K. Ojha. Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSPR/QSAR models? Journal of Chemometrics, 2018, 32, e2992. Link
42. N. Sizochenko, S. Kar, M. Syzochenko, J. Leszczynski. Predicting Thermal Conductivity Enhancement of Al2O3/water and CuO/water Nanofluids Using Quantitative Structure-Property Relationship Modeling. International Journal of Quantitative Structure-Property Relationships, 2018, 4(1), 18-27. Link
41. S. Kar, M. S. Sepulveda, K. Roy, J. Leszczynski, Endocrine-disrupting activity of poly- and perfluorinated chemicals: Exploring combined approaches of ligand and structure-based modeling. Chemosphere, 2017, 184, 514-523. Link
40. S. Kar, J. K. Roy, J. Leszczynski. Using Todays Innovative Approaches to Assure Renewable Energy for Future: In silico Designing of Power Conversion Efficient Organic Lead Dyes for Solar Cells. Nature Partner Journal Computational Materials. 2017, 3(22), 1-12. Link
39. L. S. Petrosyan, S. Kar, J. Leszczynski, B. Rasulev. Exploring simple, interpretable and predictive QSPR model of fullerene C60 solubility in organic solvents. Journal of Nanotoxicology and Nanomedicine, 2017, 2(1), 28-43. Link
38. L. Ahmed, B. Rasulev, S. Kar, J. Leszczynski, Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose. Nanoscale, 2017, 9, 10263-10276. Link
37. S. Kar, J. Leszczynski. Recent Advances of Computational Modeling for Predicting Drug Metabolism: A perspective. Current Drug Metabolism, 2017, 18(12), 1106-1122.
Link
36. K. Jagiello, A. Sosnowska, S. Kar, S. Demkowicz, M. Daśko, J. Leszczynski, J. Rachon, T. Puzyn. Geometry optimization of steroid sulfatase (STS) inhibitors - the influence on their binding energy with STS. Structural Chemistry, 2017, 28(4), 1017-1032. Link
35. S. Kar, J. K. Roy, D. Leszczynska, J. Leszczynski, Power Conversion Efficiency of Arylamine Organic dyes for Dye-Sensitized Solar Cells (DSSCs) explicit to Cobalt Electrolyte: Understanding the Structural Attributes Using Direct QSPR Approach. Computation, 2017, 5(1), 2. Link
34. W. Kolodziejczyk, S. Kar, G. A. Hill, Conformational analysis, energy profile and structural-electronic properties evaluation of Mephedrone derivatives employing quantum-mechanical models. Structural Chemistry, 2017, 28(3), 791-799. Link
33. S. Kar, N. Sizochenko, L. Ahmed, V. S. Batista, J. Leszczynski, Quantitative Structure-Property Relationship Model Leading to Virtual Screening of Fullerene Derivatives: Exploring Structural Attributes Critical for Photoconversion Efficiency of Polymer Solar Cell Acceptors. Nano Energy, 2016, 26, 677-691. Link
32. W. Kolodziejczyk, S. Kar, G. A. Hill, J. Leszczynski, A comprehensive computational analysis of cathinone and its metabolites using quantum mechanical approaches and docking studies. Structural Chemistry, 2016, 27, 1291-1302. Link
31. S. Kar, A. Gajewicz, K. Roy, J. Leszczynski, T. Puzyn, Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR. Ecotoxicology and Environmental Safety, 2016, 126, 238-244. Link
30. S. Kar, R. N. Das, K. Roy, J. Leszczynski, Can toxicity for different species be correlated? The concept and emerging applications of interspecies Quantitative Structure-Toxicity Relationship (i-QSTR) modeling. International Journal of Quantitative Structure-Property Relationships, 2016, 1(2), 22-51. Link
29. K. Roy, S. Kar, P. Ambure, On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory System, 2015, 145, 22-29. Link
28. S. Kar, A. Gajewicz, T. Puzyn, K. Roy, J. Leszczynski, Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach. Ecotoxicology and Environmental Safety, 2014, 107, 162-169. Link
27. K. Roy, S. Kar, The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (Commentary on 'Is regression through origin useful in external validation of QSAR models?'). European Journal of Pharmaceutical Sciences, 2014, 62, 111-114. Link
26. S. Kar, K. Roy, Quantification of contributions of molecular fragments for eye irritation of organic chemicals using QSAR study. Computer in Biology and Medicine, 2014, 48, 102-108. Link
25. S. Kar, K. Roy, Predictive toxicity modeling of benzodiazepine drugs using multiple in silico approaches: Descriptor-based QSTR, Group-based QSTR and 3D-toxicophore mapping. Molecular Simulation, 2015, 41, 345-355. Link
24. S. Kar, A. Gajewicz, T. Puzyn, K. Roy, Nano-Quantitative Structure-Activity Relationship Modeling Using Easily Computable and Interpretable Descriptors for Uptake of Magnetofluorescent Engineered Nanoparticles in Pancreatic Cancer Cells. Toxicol in vitro, 2014, 28, 600-606, Link
23. P. Ambure, S. Kar, K. Roy, Pharmacophore Mapping-Based Virtual Screening Followed by Molecular Docking Studies in Search of Potential Acetylcholinesterase Inhibitors as Anti-Alzheimer's Agents. Biosystems, 2014, 116, 10-20. Link
22. S. Kar, K. Roy, Predictive Chemometric Modeling and Three-Dimensional Toxicophore Mapping of Diverse Organic Chemicals Causing Bioluminescent Repression of the Bacterium Genus Pseudomonas. Industrial Engginering and Chemistry Research, 2013, 52, 17648-17657. Link
21. A. Nandy, S. Kar, K. Roy, Development and validation of regression based QSAR models for quantification of contributions of molecular fragments to skin sensitization potency of diverse organic chemicals. SAR and QSAR in Environmental Research, 2013, 24, 1009-1023. Link
20. A. Nandy, S. Kar, K. Roy, Development of classification and regression based QSAR models and in silico screening of skin sensitization potential of diverse organic chemicals. Molecular Simulation, 2014, 40, 261-274. Link
19. S. Kar, K. Roy, Prediction of milk/plasma concentration ratios of drugs and environmental pollutants using in silico tools: Classification and regression based QSARs and pharmacophore mapping. Molecular Informatics, 2013, 32, 693-705. Link
18. S. Kar, K. Roy, How far can virtual screening take us in drug discovery? Expert Opinion and Drug Discovery, 2013, 8, 245-261. Link
17. K. Roy, P. Chakraborty, I. Mitra, P. K. Ojha, S. Kar, R. N. Das, Some case studies on application of “rm2” metrics for judging quality of QSAR predictions: Emphasis on scaling of response data. Journal Computational Chemistry, 2013, 34, 1071-1082. Link
16. S. Kar, K. Roy, Prediction of hERG potassium channel blocking actions using combination of classification and regression based models: A mixed descriptors approach. Molecular Informatics, 2012, 31, 879-894. Link
15. S. Kar, K. Roy, First report on predictive chemometric modeling, 3D-toxicophore mapping and in silico screening of in vitro basal cytotoxicity of diverse organic chemicals. Toxicology in Vitro, 2013, 27, 597-608. Link
14. A. Nandy, S. Kar, K. Roy, Linear discriminant analysis for skin sensitization potential of diverse organic chemicals. Molecular Simulation, 2013, 39, 432-441. Link
13. P. K. Ojha, I. Mitra, S. Kar, R. N. Das, K. Roy, Lead hopping for PfDHODH inhibitors as antimalarials based on pharmacophore mapping, molecular docking and comparative binding energy analysis (COMBINE): A three-layered virtual screening approach. Molecular Informatics, 2012, 31, 711-718. Link
12. S. Kar, K. Roy, QSAR of phytochemicals for the design of better drugs. Expert Opinion and Drug Discovery, 2012, 7, 877-902. Link
11. K. Roy, I. Mitra, P. K. Ojha, S. Kar, R. N. Das, H. Kabir, Introduction of rm2(rank) metric incorporating rank-order predictions as an additional tool for validation of QSAR/ QSPR models. Chemometrics and Intelligent Laboratory System, 2012, 118, 200-210, Link
10. S. Kar, O. Deeb, K. Roy , Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor. Ecotoxicology and Environmental Safety, 2012, 82, 85-95, Link
9. K. Roy, I. Mitra, S. Kar, Probir Ojha, R. N. Das, Humayun Kabir, Comparative studies on some metrics for external validation of QSPR models. Journal of Chemical Information and Modeling, 2012, 52, 396-408. Link
8. S. Kar, K. Roy, First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines. Chemosphere, 2012, 87, 339-355. Link
7. S. Kar, K. Roy, Risk Assessment for Ecotoxicity of Pharmaceuticals - An Emerging Issue. Expert Opinion and Drug Safety, 2012, 11, 235-274. Link
6. S. Kar, K. Roy, Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs. Indian Journal of Biochemistry and Biophysics, 2011, 48, 111-122. Link
5. S. Kar, K. Roy, Predictive toxicology using QSAR: A perspective. J Indian Chem Society, 2010, 87, 1455-1515.
4. I. Mitra, Partha Pratim Roy, S. Kar, Probir Ojha, K. Roy, On further application of rm2 as a metric for validation of QSAR models. Journal of Chemometrics, 2010, 24, 22-33. Link
3. S. Kar, K. Roy, First report on interspecies quantitative correlation of ecotoxicity of pharmaceuticals. Chemosphere, 2010, 81, 738-747. Link
2. S. Kar, A. P. Harding, K. Roy, PLA Popelier, QSAR with Quantum Topological Molecular Similarity Indices: Toxicity of Aromatic Aldehydes to Tetrahymena pyriformis. SAR and QSAR in Environmental Research, 2010, 21, 149-168. Link
1. S. Kar, K. Roy, QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. Journal of Hazardous Material, 2010, 177, 344-351. Link
Book Chapters
18. S. Kar, J. Leszczynski, Chemometric Modeling of Emerging Materials for the Removal of Environmental Pollutants. In: Emerging Materials and Environment, (M.K. Shukla, E. Ferguson, J. Leszczynski, Eds), Springer, (2021). (In press)
17. S. Kar, J. Leszczynski, QSAR and Machine Learning Modeling of Nanomaterials Toxicity: A Risk Assessment Approach. In: Health and Environmental Safety of Nanomaterials, (James Njuguna, Ed), 2nd Edn., Elsevier (2021), pp. 417-441.
Link
16. H. Sanderson, P. M. Khan, S. Kar, K. Roy, A. Magdalene, B. Hansen, K, Connors, S, Belanger, QSAR modeling of aquatic toxicity of cationic polymers. In: Chemometrics and Cheminformatics in Aquatic Toxicology, (K. Roy, Ed), Wiley (2021), (In press)
15. S. Kar, J. Leszczynski, Aquatic toxicology databases. In: Chemometrics and Cheminformatics in Aquatic Toxicology, (K. Roy, Ed), Wiley (2021), (In press)
14. S. Kar, J. K. Roy, J. Leszczynski, Application of QSPR Modeling in Designing and Prediction of Power- Conversion Efficient Solar Cell. In: Development of Solar Cells – Theory and Experiment, (J. K. Roy, S. Kar, J. Leszczynski, Ed.), Springer (2021), pp. 167-186. Link
13. J. K. Roy, S. Kar, J. Leszczynski, Computational Screening of Organic Dye-sensitizers for Dye-sensitized Solar Cells: DFT/TDFT Approach. In: Development of Solar Cells – Theory and Experiment, (J. K. Roy, S. Kar, J. Leszczynski, Ed.), Springer (2021), pp. 187-205. Link
12. S. Kar, J. Leszczynski. Drug Databases for Development of Therapeutics Against Coronaviruses. In: In Silico Modeling of Drugs Against Coronaviruses - Computational Tools and Protocols, (K. Roy, Ed). Methods in Pharmacology and Toxicology Series, Springer, New York, NY (2021). Link
11. P. M. Khan, S. Kar, K. Roy, Ecotoxicological QSARs of Mixtures. In: Ecotoxicological QSARs, (K. Roy, Ed), springer Protocols, Humana Press (2020), pp. 437-475. Link
10. S. Ghosh, S. Kar, J. Leszczynski, Ecotoxicity Databases for QSAR Modeling. In: Ecotoxicological QSARs, (K. Roy, Ed), springer Protocols, Humana Press (2020), pp. 709-758. Link
9. S. Kar, K. Roy, J. Leszczynski, Introspection of Pharmaceuticals Ecotoxicity from the Viewpoint of In Silico Modeling. In: Computational Toxicology: Methods and Protocols, Methods in Molecular Biology, vol. 1800, (O. Nicolotti, Ed), Springer Nature (2018), pp. 141-169. Link
8. S. Kar, K. Roy, J. Leszczynski, Applicability Domain: A Step Towards Confident Predictions and Decidability for QSAR Modeling. In: Computational Toxicology: Methods and Protocols, Methods in Molecular Biology, vol. 1800, (O. Nicolotti, Ed), Springer Nature (2018), pp. 395-443. Link
7. S. Kar, K. Roy, J. Leszczynski, On error measures for validation, and uncertainty estimation of predictive QSAR models. In: Computational Nanotoxicology: Challenges, pitfalls and perspectives, (Agnieszka Gajewicz & Tomasz Puzyn, Eds), Pan Stanford Publishing, 2020, pp. 401-435. Link
6. S. Kar, S. Ghosh, J. Leszczynski, Computational Methods of Interspecies Nanotoxicity Extrapolation. In: Computational Nanotoxicology: Challenges, pitfalls and perspectives, (A. Gajewicz & T. Puzyn, Eds), Pan Stanford Publishing, 2020, pp. 437-493. Link
5. S. Kar, K. Roy, J. Leszczynski, On Application QSARs in Food and Agricultural Sciences: History and Recent Developments. In: Advances in QSAR modeling with Applications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences, (K. Roy, Ed), under the book series "Challenges and Advances in Computational Chemistry and Physics" (Series Ed: J. Leszczynski), Volume 24, Springer, 2017, 203-302. Link
4. K. Roy, S. Kar, In Silico Models for Ecotoxicity of Pharmaceuticals. In: In Silico Methods for Predicting Drug Toxicity, Methods in Molecular Biology, Vol. 1425 (Benfenati E, Ed), Springer, 2016, pp. 237-304. Link
3. K. Roy, S. Kar, The rm2 metrics for validation of QSAR/QSPR models. In: "Handbook of Research on Chemometrics: QSAR in Medicinal Chemistry (Vol I)". Eds. P. R. Duchowicz, P. M. Sivakumar, A. Mercader, Apple Academic Press, worldwide distributed by CRC Press, a Taylor & Francis Group, (2015), pp. 101-128. Link
2. K. Roy, S. Kar, Importance of Applicability Domain of QSAR Model. In: Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment. Ed. K. Roy, IGI Global, PA, Chapter 5, 2015, pp. 181-212. Link
1. K. Roy, S. Kar, How to judge predictive quality of classification and regression based QSAR models?. In: Frontiers in Computational Chemistry. Eds. Z. Haq and J. D. Madura, Bentham Science Publishers, Chapter 3 in Vol. 2, 2015, pp. 71-120. Link
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