Supratik Kar, Ph.D.

• Ph. D., Pharmacy, Jadavpur University 
• M.S., Pharmacy, Jadavpur University (Valedictorian)
• B.S., Pharmacy, Jadavpur University (Valedictorian) 
• European Union Marie-Curie Research Fellow  

• Bioinformatics (BIO2910)
• Biochemistry Lab (BCHM-4115L)
• Organic Chemistry (CHEM 2581)
• Medicinal Chemistry (CHEM4190)
• Medicinal Chemistry (STME 5625)
• Independent Chemistry Research I (CHEM 3901)
• Independent Chemistry Research II (CHEM 3902)
• Senior Honors Research (CHEM 4905)

Dr. Kar works in the field of Medicinal Chemistry incorporating Chemometrics and cheminformatics models and machine learning techniques in different aspects of Computer Aided Drug Designing (CADD), Risk assessment, and toxicity modeling of chemicals and drugs.

Dr. Kar is listed in the "Top 2% Scientists in the World for single year category” three times for the years 2020, 2022, and 2023 considering all research fields published by scientists from Stanford University in Elsevier in the years 2021, 2023, and 2024, respectively. DOI: https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/6. As per ScholaGPS, he is ranked in the Top 0.15% of scholars in the world considering all subjects based on the productivity, quality, and impact of his research for the year 2024.

Dr. Kar has published 105+ peer-reviewed research and reviews articles in Chemical Reviews, Nature Scientific Report, Green Chemistry, Expert Opinion in Drug Discovery, Expert Opinion on Drug Safety, Nano Energy, Nanoscale, Nature Computational Materials, Journal of Hazardous Materials, Chemosphere, Toxicology In Vitro, ACS Omega, Journal of Chemical Information Modeling, etc. and multiple book chapters in Springer, Willey, Elsevier etc. He has also co-authored two highly cited and popular QSAR related books entitled “Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment” (Elsevier) and “A Primer on QSAR/QSPR Modeling: Fundamental Concepts” (Springer). He also edited two Springer Books. He has published 24 book chapters to date. As per Google Scholar, he has an H-index of 43 with total citations of 7482.

Dr. Kar worked as post-doctoral research associate at ICN in the Department of Chemistry at Jackson State University, Mississippi, USA for 7 years before joining Kean University in the Fall 2022. He is also a former visiting researcher at the University of Gdańsk, Poland with the Marie-Curie research fellowship.

He serves as an associate editor for Molecular Diversity (Springer), Frontiers in Pharmacology (Frontiers), and IJQSPR (IGI-Global). He is serving as an Editorial Board member of the Journal of Clinical Medicine (MDPI), Current Drug Metabolism (Bentham), Pharmacogenomics Research and Personalized Medicine (AME), and IJANR (IGI-Global). He is also a reviewer board member for Polymers (MDPI) and Informatics and Computational Methods [Frontiers in Natural Products] (Frontiers). Dr. Kar worked as a reviewer and an editor for 75+ reputed peer-reviewed journals. For learn details, please visit his website: https://supratikkar.wixsite.com/website

Publications included are from 2023 and later:

• S. Kar*, Andrea Gallagher. In silico Modeling of Aquatic Toxicity of Organic Chemicals to Three Trout Species O. Clarkii, S. Namaycush, and S. Fontinalis: An Environmental Toxicity Data Gap Filling Study. Journal of Hazardous Materials. 2024, 136060.
• M. Nunez, S. Kar*, K. A. Rodriguez, D. Ondieki. Unraveling Ritlecitinib: An In-Depth Analysis of JAK3 Inhibitor for the Treatment of Alopecia Areata. Expert Opinion on Drug Metabolism and Toxicology. 2024
• S. Kar*, S. Yang, Introducing Third-Generation Periodic Table Descriptors To Encode Zebrafish Toxicity of Metal Oxide Nanoparticles: A Nano-qRASTR Study. Beilstein Journal of Nanotechnology, 2024, 15, 1142-1152.
• S. Yang, S. Kar*, How Safe Are Wild-Caught Salmons Exposed to Various Industrial Chemicals? First Ever In Silico Models for Salmon Toxicity Data Gaps Filling. Journal of Hazardous Materials. 2024, 477, 135401.
• A. Banerjee, S. Kar, K. Roy, G. Patlewicz, N. Charest, E. Benfenati, M.T.D. Cronin. 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 Learning. Critical Reviews in Toxicology, 2024,
• A. Bhattacharjee, S. Kar, P. K. Ojha, Unveiling G Protein-Coupled Receptor Kinase 5 Inhibitors for Chronic Degenerative Diseases: Virtual Screening Employing Machine Learning-Driven Chemometric Modeling and Molecular Simulation. International Journal of Biological Macromolecules, 2024, 269, 131784.
• S. Yang, S. Kar*, Protracted Molecular Dynamics and Secondary Structure Introspection to Identify Dual-Target Inhibitors of Nipah Virus Exerting Approved Small Molecules Repurposing. Nature Scientific Reports, 2024, 14, 3696.
• A. Gallagher, S. Kar*, Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR. Chemosphere, 2024, 349, 140810.
• S. Das; A. Samal; A. Kumar; V. Ghosh; S. Kar, P. K. Ojha, Comprehensive Ecotoxicological Assessment of Pesticides on Multiple Avian Species: Employing Quantitative Structure-Toxicity Relationship (QSTR) Modeling and Read-Across. Process Safety and Environmental Protection, 2024, 188, 39-52.
• A. Bhattacharjee, S. Kar, P. K. Ojha, First Report on Chemometrics-Driven Multilayered Lead Prioritization in Addressing Oxysterol-Mediated Overexpression of G-protein coupled receptor 183. Molecular Diversity, 2024. (In press),
• D.B. Grajales, S. Kar*. Exploring Monkeypox: From Genomic Insights to Prospective Targeted Therapies Updates. Molecular Diversity, 2024,
• S. Yang, S. Kar*, First Report on Chemometric Modeling of Tilapia Fish Aquatic Toxicity to Organic Chemicals: Risk Assessment & Toxicity Data Gap Filling. Science of the Total Environment, 2024, 910, 167991.
• D.B. Grajales, N. Sewdat, R. Leo, S. Kar*, Unveiling Abrocitinib: A Thorough Examination of the 2022 USFDA-Approved Treatment for Atopic Dermatitis (AD). Medicine in Drug Discovery, 2023, 20, 100161.
• S. Yang, S. Kar*, Application of Artificial Intelligence and Machine Learning in Early Detection of Adverse Drug Reactions (ADRs) and Drug-Induced Toxicity. Artificial Intelligence Chemistry, 2023, 100011.
• A. Gallagher, S. Kar*, M. S. Sepúlveda. Computational Modeling of Human Serum Albumin Binding of Per-and Polyfluoroalkyl Substances Employing QSAR, Read-Across, and Docking. Molecules, 2023, 28(14), 5375.
• S. Yang, S. Kar*, Computer-assisted identification of potential quinolone derivatives targeting Nipah virus glycoprotein attachment with human cell surface receptor ephrin-B2: Multistep virtual screening. Computers in Biology and Medicine, 2023, 163, 107240. Link
• R. Samanta, K. K. Pradhan, D. Sen, S. Kar*, M. Ghosh. Designing of potential Estrogen receptor binders utilizing the binding profile of active constituents present in Datura sp. Molecular Diversity, 2023, Link
• S. Yang, S. Kar*, Are We Ready to Fight the Nipah Virus Pandemic? An Overview of Drug Targets, Current Medications and Potential Leads. Structural Chemistry, 2023, 34, 2119-2137. Link
• A. Banerjee, S. Kar, Souvik Pore, K. Roy, Efficient predictions of cytotoxicity of TiO2 -based multi-component nanoparticles using a machine learning-based q-RASAR approach. Nanotoxicology, 2023, Link
• K. Khan, S. Kar*, K. Roy, Are We Ready to Combat the Ecotoxicity of COVID-19 Pharmaceuticals? An In Silico Aquatic Risk Assessment. Aquatic Toxicology, 2023, 256, 106446 Link

To view the complete list of publications, please visit the following s:

Google Scholar ; ORCID ; Scopus

To view the editorial and review activity, please visit the following :

Web of Science 

• USEPA (Federal) fund received in Collaboration with Purdue University, Indiana (a R1 institute). Subaward No. 16200050-034 (PTE Federal award No. 84045601). Title: “Protein Binding Affinity as the Driver for Studying PFAS Mixture Toxicity”. Project duration: 09-01-2022 to 08-31-2025. Total funds granted: $725K. I received: $166,224 (Direct funding: $115,440, Indirect funding: $50,784)
• Student Partnering Faculty (SPF) grant from Kean University for the year 2024-25, funded amount $12.5K
• Received around $1000 from the Dean’s office of Kean University as additional funds for performing research in the year 2024.
• Received around $1500 from the Dean’s office of Kean University as additional funds for performing research in the year 2023.
• Seed fund for around $20K from the Dean’s office of Kean University in the year 2022.