Wenjun Xie, Ph.D.

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Group website: https://evocatalysis.github.io

Wenjun Xie is an Assistant Professor in the Department of Medicinal Chemistry at the University of Florida. He earned his B.S. in Chemistry and Statistics and Ph.D. in Physical Chemistry from Peking University, followed by postdoctoral research at MIT and the University of Southern California. His research combines molecular simulation, artificial intelligence, and experimental methods to study enzyme catalysis, evolution, and design. His group has shown that generative AI models trained on natural enzyme sequences can accurately predict the impact of mutations on catalytic activity, enabling the rational improvement of enzymes shaped by billions of years of evolution. These findings led to the formulation of the concept of evolutionary catalysis—the idea that fundamental principles of catalytic efficiency are encoded in evolutionary sequence patterns and can be uncovered using generative AI.

My lab integrates molecular simulation, AI, and experimental approaches to investigate enzyme catalysis, evolution, and design. We discovered that generative AI models trained on natural enzyme sequences can accurately predict the effects of mutations on catalytic activity (PNAS, 2022; JACS, 2025). This predictive power enables the rational enhancement of enzymes that have already undergone billions of years of evolution (PNAS, 2023). Based on these findings, we propose the concept of evolutionary catalysis—that the principles underlying catalytic efficiency are embedded in evolutionary sequence patterns and can be decoded using generative AI (Natl Sci Rev, 2023). Our research benefits from the collaborative synergy between computational scientists, life scientists, and engineers. Our lab focuses on three main areas: (1) evolutionary catalysis; (2) rational enzyme engineering; and (3) AI-driven drug discovery. For additional details, please visit our group’s website at https://evocatalysis.github.io.