Design. Make. Test. Learn.

Malaria continues to demand therapeutic candidates with new chemical structures and mechanisms. My doctoral research investigates orbitides, head-to-tail cyclic peptides that provide a compact framework for systematic medicinal-chemistry optimization.

I synthesize and evaluate orbitides to determine how amino-acid sequence, stereochemistry, aromatic substitution, ring size, and backbone modification influence antiplasmodial activity. The program also examines mammalian-cell selectivity, plasma and protease stability, and preferred solution conformations.

Core questions

Which structural features govern activity against drug-resistant Plasmodium falciparum? Can conformational control improve potency, selectivity, and stability? Which analogues justify broader strain profiling and mechanistic investigation?

The influenza A matrix protein M1 coordinates viral assembly through dynamic protein-protein interactions. A complementary research direction considers whether peptide candidates could engage M1 interaction surfaces and disrupt contacts required for productive assembly.

This framework brings together artificial intelligence, machine learning, peptide-sequence design, structure prediction, interface analysis, and molecular simulations. Biophysical validation would provide the experimental test needed to distinguish computational predictions from credible molecular probes and therapeutic starting points.

Computation is most valuable when it sharpens experimental decisions. I integrate artificial intelligence and machine learning with molecular docking, virtual screening, structure prediction, conformational sampling, and molecular dynamics to prioritize molecules, interpret observations, and design the next experiment.

My toolkit includes AI-enabled peptide-design workflows, Glide, AutoDock Vina, MOE, GROMACS, Desmond, MODELLER, PyMOL, VMD, and Python-based automation. The platform spans peptides, small molecules, protein-ligand interactions, and protein-protein interfaces.