Dr Zeeshan Arif

About

Dr. Zeeshan Arif is a distinguished computational chemist, toxicologist, and bioinformatician with extensive expertise in quantum mechanics/molecular mechanics (QM/MM) approaches, including Density Functional Theory (DFT) and Molecular Dynamics Simulations, applied to Structure-Based Drug Discovery (SBDD). His research spans a broad spectrum, encompassing molecular descriptor generation, QSAR/QSTR predictive modeling, cheminformatics for toxicity endpoint prediction, computational toxicology, and chemical space exploration. With a focus on translational bioinformatics, Dr. Arif excels in biomarker identification, ligand-protein interaction studies, and predictive toxicology. His innovative approaches and multidisciplinary expertise contribute significantly to advancing drug discovery and toxicity prediction, solidifying his position as a thought leader in the field.

Employment

• Indian Institute of Toxicology Research Research And IT Assistant
  2024 - Present
• Indian Institute of Toxicology Research Senior Project Associate
  2022 - 2024
• RMIT University PhD (Applied Chemistry)
  2019 - 2023
• Indian Institute of Toxicology Research PhD (Biological Sciences)
  2016 - 2023

Education

• RMIT University PhD
  2018 - 2024
• Aligarh Muslim University Intermediate
  2005 - 2006
• Faizabad Public School High School
  2003 - 2004

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• Computational and Biological Insights for Anti-Cancer Effects of Karanjin through AR/ER Modulation, ROS Generation and Cell Cycle Arrest in Prostate Cancer
  Journal of Traditional and Complementary Medicine 2025 DOI: 10.1016/j.jtcme.2025.03.008
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• Biological evaluation of selected phytochemicals as a multi-targeted antitumor, antiproliferative and apoptotic agents in cancer
  Pharmacological Research - Natural Products 2024 DOI: 10.1016/j.prenap.2024.100097
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• A Review on Nanotechnology-Based Interventions on Advancing COVID-19 Diagnosis, Prevention and Treatment
  Journal of Nanomedicine, MedDocs Publishers LLC 2024
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• Developing Quantitative Structure Toxicity Relationship (QSTR) Models using Relevant Physico-chemical Descriptors for Predicting Chemical Toxicity
  2024 DOI: 10.25439/rmt.25906429
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• Structure based docking and biological evaluation towards exploring potential anti-cancerous and apoptotic activity of 6-Gingerol against human prostate carcinoma cells
  2024 DOI: 10.1186/s12906-023-04269-1
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• “Aquatic-quantitative structure toxicity relationships (Aqua-QSTR) model development utilizing electrophilicity-based charge transfer descriptor”
  9th International Food Convention (IFCoN 2023) at CSIR-CFTRI Campus Mysuru, Karnataka, India 2023
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• “Aquatic-quantitative structure toxicity relationships (Aqua-QSTR) model development utilizing electrophilicity-based charge transfer descriptor”
  Conference on Sustainable and Emerging Technologies for Food Safety and Environmental Toxicology (SET-FEST) at CSIR-Indian Institute of Toxicology Research, Lucknow, U.P., India 2023
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• Electrophilicity-based charge transfer for developing aquatic-quantitative structure toxicity relationships (Aqua-QSTR)
  Theoretical Chemistry Accounts 2023 DOI: 10.1007/s00214-023-02977-y
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• “Computational Toxicity Characterization of Zinc Oxide clusters (ZnO)n=(1-12)”
  Indian Conference on Bioinformatics 2021 (INBIX’2021), virtually organized by CSIR-NEIST and Bioclues.org 2021
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• Chemical Reactivity and Skin Sensitization Studies on a Series of Chloro- and FluoropyrrolesA Computational Approach
  2021 DOI: 10.1021/acsomega.1c02436
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