Senior Research Associate · Computational Drug Discovery · Takeda Pharmaceuticals
My research lies at the intersection of geometric deep learning, cheminformatics, structure-based drug design, and agentic AI for drug discovery. I'm interested in developing machine learning and AI systems that respect the physical symmetries of molecular systems, leverage structural and biochemical knowledge, and support decision-making across chemical and biological spaces.
- Equivariant and geometric neural networks for 3D molecular modeling
- Graph-based representations of protein–ligand interactions
- Predictive modeling for drug discovery (binding affinity, ADMET, selectivity)
- Uncertainty quantification and robust learning on sparse bioassay data
- Agentic AI systems for molecular design, prioritization, and discovery workflows
