Aravindhan Ganesan MSc PhD #
- Dr. Aravindhan Ganesan is Research Assistant Professor at University of Waterloo’s School of Pharmacy
- He is a leading expert in computational molecular biosciences and drug discovery
- His work bridges molecular pharmacology, computational tool development, and structural biology, with applications in neurodegeneration, cancer, and COVID-19 therapeutics.
University of Waterloo
Credit: uwaterloo.ca
Introduction #
Dr. Ganesan’s research focuses on molecular recognition, small molecule therapeutic design, and advanced computational tools for in silico structural biology. His multidisciplinary approach combines molecular dynamics simulations, in silico mutagenesis, and protein-protein modeling to unravel biological events at the molecular level, enabling rational drug design.
Professional Background and Achievements #
Education #
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PhD in Computational Chemistry Swinburne University of Technology, Melbourne, Australia (2008–2012) Thesis: Structure-property relationships of amino acids.
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MSc in Bioinformatics Annamalai University, India (2002–2007)
Postdoctoral Research #
- German Research School of Simulation Sciences, Jülich, Germany Fellowship: Deutscher Akademischer Austauschdienst (DAAD) (2011–2012)
- Australian National University, Canberra, Australia Fellowship: Endeavour Postdoctoral Research Fellowship (2014)
Current Positions #
- Research Assistant Professor University of Waterloo, School of Pharmacy
- Assistant Professor Wilfrid Laurier University, Department of Chemistry and Biochemistry
Research Focus #
Key Areas #
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Molecular Recognition in Biological Systems
- Computational methodologies for cannabinoid receptor pharmacology.
- Aggregation of hnRNPA1 proteins in multiple sclerosis.
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Small Molecule Therapeutics
- Neurodegeneration, cancer, and COVID-19 drug design.
- Novel scaffolds (eg 4501-series, N17-series) targeting CTLA-4 and PD-L1 pathways in cancer immunotherapy.
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Computational Tools for Structural Biology
- Molecular dynamics simulations and protein-protein modeling.
- SARS-CoV-2 Mpro inhibitors: Identified the role of a lateral pocket in enhancing ligand affinity for antiviral drug development.
Collaborations #
- University of Waterloo: Primary affiliation for research programs.
- Indian Institutions: Pondicherry University, PSG Institute of Advanced Studies, Chettinad Academy of Research & Education.
- Industry: Applied Pharmaceutical Innovation
- Quantum Research: Institute for Quantum Computing.
Awards and Honors #
- German DAAD Research Fellowship (2011–2012)
- Australian Endeavour Postdoctoral Fellowship (2014)
- NSERC Discovery Grant (Cannabinoid receptor pharmacology)
- New Frontiers in Research Fund-Exploration Grant (hnRNPA1 protein aggregation)
- Canada First Research Excellence Fund (CFREF-TQT) (Computational drug discovery)
- Cancer Research Society Operating Grant (Small molecule inhibitors of VISTA for cancer immunotherapy)
Publications #
| Title | Journal | Year | Link |
|---|---|---|---|
| Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of a lateral pocket in enhancing the ligand affinity | Scientific Reports | 2021 | link |
| hnRNP A/B Proteins: An encyclopedic assessment of their roles in homeostasis and diseases | Biology | 2021 | link |
| Structure-based virtual screening, molecular dynamics, and binding affinity calculations of potential phytocompounds against SARS-CoV-2 | Journal of Biomolecular Structure and Dynamics | 2021 | link |
| Targeting B7-1 in Immunotherapy | Medicinal Research Reviews | 2019 | link |
| Comprehensive in vitro characterization of PD-L1 inhibitors | Nature | 2019 | link |
| Revealing the atomistic details behind the binding of B7-1 to CD28 and CTLA-4 | Biochimica et Biophysica Acta-General Subjects | 2018 | link |
| A mathematical modelling tool for unravelling the antibody-mediated effects on CTLA-4 interactions | BMC Medical Informatics and Decision Making | 2018 | link |
| Molecular ’time-machines’ to unravel key biological events for drug design | WIREs: Computational Molecular Science | 2017 | link |
| Simple design of an enzyme inspired supported catalyst based on a catalytic triad | Chem | 2017 | link |