CBE Colloquia - Computational Molecular Design Across Scales

The design and synthesis of molecules and materials with tailored structures and functions have long been central goals of chemistry. Advances in synthesis, characterization, and computation have steadily expanded the range of accessible molecular systems. Yet rational molecular design remains a fundamental challenge, requiring us to understand the interdependence of molecular structure, synthesis, and properties while navigating vast chemical spaces. This challenge becomes increasingly complex as we move from small molecules to synthetic polymers and sequence-defined biomacromolecules. My group develops computational approaches to address these challenges and enable systematic molecular design across these systems.

At the small-molecule level, we develop multimodal and agentic AI methods to transform reaction information in the scientific literature into structured, machine-readable data, and chemically informed algorithms to guide multistep retrosynthetic planning. For synthetic polymers, we pursue complementary design approaches using machine learning and generative methods to identify monomers and repeat units with targeted properties, and accelerated mechanistic simulations to predict and control the molecular-weight and sequence distributions arising from polymerization. At the biomacromolecular level, we use large-scale structure prediction, topological analysis, and generative modeling to discover and design entangled proteins, with selected structures and materials realized experimentally. 

Together, we seek to connect chemical information, molecular structure, synthesis, and properties within a unified computational framework, and to extend systematic molecular design toward increasingly complex chemical and biological systems.