Dr. Parasuraman Pavadai
A passionate scientist dedicated to discovering tomorrow's medicines.
A research centered on designing, discovering and developing novel therapeutics through cutting-edge computational approaches with experimental validation.
My professional career is dedicated to advancing the frontiers of pharmaceutical sciences through rigorous research and a commitment to academic excellence. The core of my research program involves a multifaceted approach integrating structural and ligand-based drug design, molecular dynamic simulations, and quantum mechanics with experimental organic synthesis and pharmacological screening. This synergy between in silico modeling and laboratory validation facilitates an efficient and precise pipeline for drug discovery.
Scholarly Impact at a Glance
In Scopus / SCI Listed Journals.
Reflecting the influence of published work.
Mentorship across PhD, M.Pharm, and B.Pharm levels.
Translating research into novel IP.
Core Academic Pillars
Research Program
My research leverages computational tools to model molecular interactions and predict therapeutic efficacy, bridging theoretical chemistry with practical application.
Explore ResearchScientific Publications
A comprehensive record of contributions to high-impact literature, with articles in journals published by Nature, Elsevier, and Springer.
View PublicationsTeaching & Mentoring
A dedication to higher education focused on developing future pharmaceutical scientists through rigorous coursework and direct mentorship.
Learn My ApproachProudly Announcing PharmDEX
Excipient Compatibility Discovery Engine
We are proud to announce the launch of PharmDEX, a clinical-grade platform developed in our research lab. Leveraging state-of-the-art machine-learning models to predict Active Pharmaceutical Ingredient (API)–excipient compatibility, PharmDEX accelerates pharmaceutical formulation research with confidence-scored results.
By simply inputting chemical structures (such as SMILES strings), formulation scientists can instantly screen candidates against a master library of 690+ excipient compounds. This advanced prediction engine integrates quantitative compatibility scoring, optimizing candidate selection and minimizing costly laboratory trial-and-error.
