Zhuoran Qiao

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

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
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

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Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning–based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.
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)

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We introduce a machine learning method in which energy solutions from the Schrödinger equation are predicted using symmetry adapted atomic orbital features and a graph neural-network architecture. OrbNet is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison [Int. J. Quantum Chem. (published online) (2020)], OrbNet predicts energies within chemical accuracy of density functional theory at a computational cost that is 1000-fold or more reduced.
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

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We confirmed that monolayer water confined by parallel graphene sheets spontaneously crystallizes from a structurally and dynamically heterogeneous liquid phase under moderate supercooling via direct molecular dynamics simulation. Square-lattice-like geometric order is observed at the early stage of nucleation and is preserved during the entire nucleus growth process. The diffusion coefficient and free energy profile in the cluster space extracted from a Bayesian trajectory analysis agree well with the classical nucleation theory (CNT) prediction and yield thermodynamic quantities exhibiting linear temperature dependence. The effectiveness of maximum cluster size as the descriptor of ice nucleation dynamics in the CNT framework can be attributed to the dynamical time scale decoupling and strong structural pattern dependence of density fluctuation in the liquid phase.