Research
I work toward a future where therapeutics are not just discovered, but also designed: AI models that propose novel molecules while anticipating biological response and host safety. The goal is a unified workflow—generation, prediction, and experimental feedback—so candidates are potent by design and safer by default.
Closed-loop discovery: generate → predict → test → learn.
Focus areas
What I build and apply day-to-day.
Generative design
Create novel small molecules optimized for target activity and developability—not just rediscover known scaffolds.
Safety-aware modeling
Treat toxicity as a first-class objective by embedding safety predictors directly into prioritization and design loops.
AMR-focused discovery
Build adaptive ML frameworks to identify novel mechanisms and candidates against resistant Gram-negative pathogens.
Multi-modal mechanism of action
Integrate structure, transcriptomics, and high-content imaging to infer mechanism and guide experimental follow-up.