Yuhui Hong

Yuhui Hong

Postdoctoral Fellow at Noble Lab (2025 - Present)
University of Washington
Advised by Professor William Noble

Ph.D. in Computer Science (2020-2025)
Indiana University Bloomington
Advised by Professor Haixu Tang

We "see" small molecules through analytical instruments (e.g., LC-MS, GC-MS, etc.) and analyze them using computational methods. Delighting the "dark matter" of chemical space—the vast number of unknown compounds—remains a significant challenge in the field. During my PhD, I explored small molecule identification through two approaches: (1) predicting LC-MS/MS and molecular properties from 3D conformations as a supplementary library for reference used in searching, and (2) predicting chemical formulas directly from LC-MS/MS, which goes beyond traditional database-dependent approaches with an understanding of more complex patterns in spectra. My ultimate aim is to design reliable computational methods to tackle real-world problems and enhance scientific research.

Prior to joining Indiana University Bloomington, I received my B.S. in Computer Science from Xidian University, China, in 2019, and subsequently worked as a research assistant at Xi'an Jiaotong University from 2019 to 2020 under the guidance of Professor Yaochen Li.

Please feel free to get in touch!

News 📰

- [07/07/2025] I will join Noble Lab at University of Washington as a Postdoctoral Scholar in August 2025.

- [05/09/2025] Recipient of the the Luddy Outstanding Research Award.

- [03/04/2025] Our work, A Task-Specific Transfer Learning Approach to Enhancing Small Molecule Retention Time Prediction with Limited Data, has been selected for an oral presentation at ASMS 2025.

Selected Publications ✨

Preprints

tstl_rt
A Task-Specific Transfer Learning Approach to Enhancing Small Molecule Retention Time Prediction with Limited Data
Yuhui Hong, & Haixu Tang (2025).
bioRxiv 2025.06.26.661631. [Preprint] [Code]
TSTL (Task-Specific Transfer Learning) is introduced as a training strategy for predicting retention times in various LC systems with limited training data. Evaluated across 6 benchmark datasets from different LC systems using 5 deep neural network architectures, TSTL achieved significant improvements in prediction accuracy, increasing average R² from 0.587 to 0.825 with superior data efficiency.
fiddle
FIDDLE: a Deep Learning Method for Chemical Formulas Prediction from Tandem Mass Spectra
Yuhui Hong, Sujun Li, Yuzhen Ye, & Haixu Tang (2024).
bioRxiv 2024.11.25.625316. [Preprint] [Code] [PyPI package - msfiddle]
FIDDLE (Formula IDentification by Deep LEarning) is introduced as a deep learning-based method for identifying chemical formulas from MS/MS data. It is trained on over 38,000 molecules and 1 million MS/MS spectra collected under various conditions, including collision energy and precursor types, using Quadrupole Time-of-Flight (QTOF) and Orbitrap instruments.

Peer-reviewed Articles

ac.3c04028
Enhanced Structure-Based Prediction of Chiral Stationary Phases for Chromatographic Enantioseparation from 3D Molecular Conformations
Yuhui Hong, Christopher J Welch, Patrick Piras, & Haixu Tang (2024).
Analytical Chemistry, 96(6), 2351-2359. [Paper] [Code]
3DMolCSP leverages a 3D molecular conformation representation algorithm, alongside a dataset of over 300k enantioseparation records. This approach significantly improves enantioselectivity predictions, enabling more efficient and informed decisions in chiral chromatography.
bioinfo.btad354f1
3DMolMS: Prediction of Tandem Mass Spectra from Three Dimensional Molecular Conformations
Yuhui Hong, Sujun Li, Christopher J Welch, Shane Tichy, Yuzhen Ye, & Haixu Tang (2023).
Bioinformatics, btad354. [Paper] [Code] [Documentation] [PyPI package - molnetpack] [Workflow on Konia]
3DMolMS is a deep neural network model that predicts MS/MS spectra from 3D conformations. The learned molecular representation also enhances predictions of chemical properties, such as elution time and collisional cross section, aiding compound identification.

Presentations and Talks 💡

Teaching 👩🏽‍🏫

Professional Services 🙌