Machine Learning Aided Rational Drug Discovery and Design

We built a high-performance discovery pipeline that generated and evaluated drug candidates before they ever reached a lab bench. The system screened compounds for stability, binding behavior, and synthesis feasibility using large-scale simulation and machine learning, which reduced unnecessary wet-lab work and helped researchers focus on the most promising candidates faster. The result was a more efficient AI for drug discovery pipeline, moving from computational exploration to real scientific decision-making. We've built scientific computing systems that help research teams move faster with less guesswork, combining machine learning, simulation, and data pipeline design into practical tools for biotech discovery.