Computation in the service of therapeutics.
From disease biology to experimentally testable leads. Six themes, one toolkit, and a signature discovery pipeline.
Shortening the distance between a biological question and a molecule worth testing.
Across infectious and non-infectious diseases, I integrate bioinformatics, cheminformatics, omics analysis, molecular modelling, and artificial intelligence to identify and prioritise therapeutic targets, then to find and refine the bioactive molecules most likely to act on them. The work spans AI and machine learning for predictive modelling and QSAR; structure- and ligand-based discovery; immunoinformatics and reverse vaccinology; omics and network pharmacology; natural-product and peptide therapeutics; and protein engineering.
The aim throughout is practical: computationally informed solutions that are reproducible, interpretable, and ready to hand to experimental collaborators.
Six lines of computational enquiry
AI & Machine Learning in Biomedicine
Predictive modelling, QSAR, and data-driven therapeutic discovery.
Drug Discovery & Molecular Modelling
Docking, pharmacophores, virtual screening, dynamics, ADME.
Immunoinformatics & Reverse Vaccinology
Epitope selection and multi-epitope vaccine design.
Omics, Networks & Biomarkers
Transcriptomics, proteomics, and network pharmacology.
Natural Products, Peptides & Phytotherapeutics
Bioactive peptides and phytochemical therapeutics.
Protein Engineering & Functional Annotation
Structure/function prediction and enzyme stability.
From biological target to testable lead molecule.
A five-stage computational pipeline sits at the centre of every project, carrying a disease question from its molecular origin to candidates ready for the bench.
Target Identification
Define and prioritise therapeutic targets.
Virtual Screening
Screen large libraries to find promising hits.
Lead Optimisation
Improve potency, selectivity, and drug-likeness.
ADMET Prediction
Evaluate safety, PK/PD, and developability.
Drug Candidates
Advance the best candidates to the bench.
Methods used across projects
Target discovery and prioritisation from biological and disease data
Protein structure modelling and binding-site analysis
Molecular docking, virtual screening and drug repurposing
Ligand-based design, pharmacophore modelling and QSAR
ADMET profiling and molecular dynamics simulations
Natural-product research and AI-assisted lead optimisation
Featured projects
Each project is summarised the same way: question, methods, contribution, output. Full write-ups are added as work is published.
Write-up in preparation.
Write-up in preparation.
Write-up in preparation.