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Continuous Learning

From computational drug design to supply chain analytics and now AI/LLM tooling — a career shaped by curiosity and interdisciplinary thinking.

Certifications

A selection of professional certifications spanning supply chain management, data analytics, SAP, and AI tools.

Certification Certification Certification Certification Certification Certification

Skills Overview

A snapshot of technical proficiencies built across research, SCM, and data analytics roles.

Skills Overview

Research Publications & Presentations

A selection of peer-reviewed publications and conference presentations from my research career in computational drug design and natural product chemistry.

  • Murugesan, J.R., Louis V., Ayotte Y., & LaPlante S.R. Building and Curating Fragment Libraries for Discovering Drug Leads. North American UGM & CCG Conference; June 2018, Montreal, QC, Canada.
  • Ayotte Y., Murugesan J.R., Bilodeau F., Larda S., et al. NMR toolbox for drug design – from hit identification to lead optimization. 31st Protein Society Conference; July 2017, Montreal, QC, Canada.
  • Ayotte Y., et al. Fragment lead discovery: Target and phenotypic screening approaches. 5th Drug Discovery & Therapy World Congress; July 2017, Boston, MA, USA.
  • Murugesan J.R., Shahout F., Amar M.D., et al. Revealing dye and dye-drug aggregation into nano-entities at the atomic level by NMR. Dyes and Pigments 153 (2018): 300–306.
  • Prabhakaran K., et al. & Murugesan J.R. Polyketide natural products, acetogenins from graviola (Annona muricata L). Curr. Pharm. Des. 2016, 22(34), 5204–5210.
  • Paul J., Ramasamy G., Murugesan J.R., & Loganathan A. Anti-cancer activity of graviola vs various cancer cell lines. Curr. Top. Med. Chem. 2013, 13(14), 1666–1673.
  • Murugesan J.R., Ray A., Naik D., Sanyal D.N., & Shah D. Computational target analysis using BioRuby and in silico screening of herbal lead compounds against pancreatic cancer using R. Curr. Drug Metab. 2014, 15(5), 535–543.
  • Murugesan J.R. & Saradha N.M. Computational approaches to screen candidate ligands with anti-Parkinson's activity using R programming. Curr. Top. Med. Chem. 2012, 12(16), 1807–1814.
  • Murugesan J.R. & Sharma S. Computational models for 5αR inhibitors for treatment of prostate cancer. Curr. Comput. Aided Drug Des. 2011, 7(4), 231–237.
  • Bhattacharjee B., Murugesan J.R., et al. Review of complex network and gene ontology in pharmacology approaches for colon cancer. Curr. Bioinform. 2011, 6(1), 44–52.
  • Bhattacharjee B., Murugesan J.R., et al. Screening of novel inhibitors for MEK1 induced breast cancer — an in-silico approach. EMB.net 2009, 16(3), 025–028.