Generative AI for Molecular Design: From Drugs to Sustainable Materials

R. Mercado

Generative and predictive machine learning models are reshaping how we explore chemical compound space, yet meaningful impact requires models that can reason across molecular scales, handle heterogeneous data, and adapt to real-world design constraints. In this talk, I will present recent work from our group on AI-driven molecular design, spanning small-molecule therapeutics, targeted protein degraders, and sustainable materials for emerging technologies. We are developing approaches that combine generative modeling, structure-aware learning, and chemically informed representations to support tasks ranging from reaction and synthesis planning to property optimization and protein-ligand complex modeling. Beyond drug discovery, I will highlight efforts to apply these techniques to materials challenges relevant to semiconductors and manufacturing sustainability. Our work is carried out in close collaboration with industrial partners across pharmaceuticals and advanced materials (including AstraZeneca, Intel, and Merck), ensuring that the methods we develop translate to practical design workflows.