Zhuoran Qiao

Physics-Informed AI for Biochemical Discovery.

I am a Senior Machine Learning Scientist at Iambic Therapeutics, currently leading research on biomolecular structure prediction and generative AI for drug discovery. I earned my Ph.D. degree in Chemistry from Caltech CCE with a minor in Quantum Science and Engineering (Thesis), where I was fortunately advised by Prof. Anima Anandkumar and Prof. Thomas Miller. My past research centers around developing physics-driven geometric learning approaches to tackle complex problems in chemistry and structural biology.

I earned my BSc from Peking University in 2019. As an undergraduate student I worked in the Gao Group at PKU CCME, where I studied the statistical mechanics of confined soft matters.

Selected publications

  1. State-specific protein–ligand complex structure prediction with a multiscale deep generative model
    Qiao, Zhuoran, Nie, Weili, Vahdat, Arash, Miller III, Thomas F, and Anandkumar, Animashree
    Nature Machine Intelligence 2024
  2. Informing geometric deep learning with electronic interactions to accelerate quantum chemistry
    Qiao, Zhuoran, Christensen, Anders S., Welborn, Matthew, Manby, Frederick R., Anandkumar, Anima, and Miller, Thomas F.
    Proceedings of the National Academy of Sciences 2022
  3. OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
    Qiao, Zhuoran, Welborn, Matthew, Anandkumar, Animashree, Manby, Frederick R, and Miller III, Thomas F
    The Journal of Chemical Physics 2020 (Editor’s Pick)
  4. Ice nucleation of confined monolayer water conforms to classical nucleation theory
    Qiao, Zhuoran, Zhao, Yuheng, and Gao, Yi Qin
    The journal of physical chemistry letters 2019