Yuhui Hong

Yuhui Hong

Ph.D. candidate in Computer Science
Indiana University Bloomington
Advised by Prof. Haixu Tang

My research explores the intersection of deep learning, bioinformatics, and cheminformatics, with a focus on advancing the identification of small molecules in two pathways. The first involves predicting tandem mass spectra and other molecular properties from 3D structures, addressing gaps—often referred to as the "dark matter"—in existing spectral reference libraries. The second approach moves beyond the traditional reliance on database-driven methods by predicting compounds directly from tandem mass spectra. My aim is to use computational and machine learning methods to tackle real-world problems and enhance scientific research. Additionally, I am passionate about developing reliable and interpretable neural networks for real-world applications.

Please feel free to get in touch!

Selected Publications ✨

The complete list of publications can be found on [Google Scholar].

Books

acsinfocus.7e8012
Neural Networks for Chemists
Qingyang Xiao, Kaiyuan Liu, Yuhui Hong & Haixu Tang (2024).
American Chemical Society. [Primer]
This primer introduces the basics of neural networks, guiding students, researchers, and professionals to harness their potential. It covers foundational concepts, fully connected networks, advanced architectures, and case studies, illustrating their impact on fields like chemistry, healthcare, and beyond.

Preprint

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]
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-reviewd Articles

anchem
Machine Learning in Small-Molecule Mass Spectrometry
Yuhui Hong, Yuzhen Ye, & Haixu Tang (2025).
(Accepted by Annual Review of Analytical Chemistry, to be published on May 2025). [Paper]
This review highlights how machine learning is transforming small molecule mass spectrometry by: (a) predicting MS/MS spectra and properties to expand reference libraries, (b) enhancing spectral matching with automated pattern extraction, and (c) directly predicting molecular structures from MS/MS spectra when reference data is unavailable.
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] [PyPI package - molnetpack] [Web service on GNPS] [Inference service 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 🙌