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Core Research Areas

Seven interconnected domains spanning computational chemistry, AI-driven discovery, medicinal synthesis, and natural product pharmacology.

01

Computer-Aided Drug Design

High-throughput molecular docking, molecular dynamics simulation, free-energy perturbation, virtual screening, and QSAR modelling using Maestro (Schrödinger), AutoDock Vina, BIOVIA Discovery Studio and GROMACS. Structure-based and ligand-based design workflows applied to kinase, GPCR, enzyme and HDAC targets.

Molecular Docking MD Simulation QSAR / QSPR Virtual Screening Pharmacophore Modelling ADMET Prediction
02

HDAC Inhibitor Design

Rational design and synthesis of histone deacetylase (HDAC) inhibitors — particularly piperidine hydroxamate scaffolds — for apoptosis induction in cervical cancer cell lines. Integrates classical medicinal chemistry optimisation with computational scoring functions to identify potent, isoform-selective candidates.

HDAC Isoform Selectivity Hydroxamic Acid Warheads Apoptosis Induction Cervical Cancer Zinc Chelation Chemistry
03

Marine Bioactive Compounds

Discovery and computational profiling of antiviral and antimicrobial agents from marine organisms. Focus on human metapneumovirus (HMPV), Nipah virus and Zika virus targets. Combines in-silico screening of natural product databases with in-vitro validation to identify lead molecules from unexplored marine biodiversity.

Antiviral Agents HMPV / Nipah / Zika Marine Natural Products Antimicrobial Natural Product Databases
04

Medicinal Chemistry & Synthesis

Design and multi-step synthesis of flavonoid derivatives, chromone analogues, pyrazoline scaffolds, and heterocyclic compounds with full spectroscopic characterisation (NMR, IR, mass spectrometry). Systematic structure–activity relationship studies guide optimisation toward improved potency and selectivity against cancer, diabetes, and inflammatory targets.

Flavonoids Chromone Analogues Pyrazolines Heterocyclic Scaffolds SAR Studies Spectroscopic Characterisation
05

AI & Machine Learning in Drug Discovery

Application of machine learning and explainable AI (XAI) approaches to cheminformatics challenges: activity-cliff detection, scaffold-hopping, molecular fingerprint visualisation, and multi-task predictive modelling. Python-based workflows using RDKit, scikit-learn, and deep learning frameworks enable data-driven lead optimisation.

Explainable AI (XAI) Cheminformatics Molecular Fingerprints Activity-Cliff Analysis RDKit / scikit-learn Deep Learning
06

Drug Discovery & Pharmacology

Integrated in-silico, in-vitro and in-vivo pharmacological evaluation for anti-diabetic, anti-inflammatory, antimicrobial and anticancer targets. Repurposing of FDA-approved drugs via computational screening (Nipah, SARS-CoV-2 targets), coupled with animal model studies to establish efficacy and toxicity profiles.

Drug Repurposing Anti-diabetic Targets Anticancer Screening In-vivo Pharmacology ADMET / Toxicity
07

Spectroscopic Analysis & Structural Chemistry

Structure elucidation and rigorous characterisation of synthesised compounds using ¹H and ¹³C NMR, FT-IR, UV-Vis spectroscopy, and high-resolution mass spectrometry (HRMS). Crystallographic analysis and conformational studies underpin reliable structure–activity correlations across all synthesis programmes.

¹H / ¹³C NMR FT-IR HRMS UV-Vis Conformational Analysis