Georgia Institute of Technology College of Sciences

Artificial intelligence (AI) has become a transformative force in scientific discovery---known as AI for Science---with profound impact on computational molecular design, as highlighted by the 2024 Nobel Prize in Chemistry. Due to their remarkable capability in analyzing complex structures, message-passing neural networks and diffusion- and flow-based generative models stand out as effective tools for molecular property prediction and structure generation. However, message-passing neural networks struggle to efficiently integrate multiscale molecular features and complex 3D geometry for accurate property prediction, and (2) the generative processes of generative models are often computationally intensive and error-prone. 

In this talk, I will present our recent advances toward overcoming these limitations: (1) multiscale graph representations and message-passing architectures for efficient and accurate molecular learning, and (2) one-step flow-based generative models that enable high-fidelity molecule generation with dramatically reduced computational cost.