Kyo Bin Kang

Biosketch

Kyo Bin Kang is an Associate Professor at the College of Pharmacy, Sookmyung Women’s University in Seoul, Korea. Since establishing his lab in 2018, he has focused on applying computational mass spectrometry-based metabolomics to study specialized metabolites produced by bacteria, fungi, and plants.

Trained as a natural product chemist, he completed his PhD at Seoul National University in 2016, concentrating on the purification and structural elucidation of phytochemicals. During his first postdoctoral research at the same institution, he developed a strong interest in mass spectrometry-based metabolomics as a powerful tool in natural product discovery. To deepen his expertise, he joined the Dorrestein Lab at UC San Diego for his second postdoctoral training, where he became a devoted proponent of computational mass spectrometry and open science. His lab now investigates not only the discovery of novel or bioactive compounds, but also the ecological and biochemical logic behind the chemical diversity of natural products—how and why they are biosynthesized, and how organisms biotransform and detoxify foreign molecules. Metabolomics is central to generating hypotheses across all these research directions. Kyo Bin is also actively engaged in infrastructure development for the Korea MetAbolomics data repository (KMAP), striving to align it with the FAIR principles and harmonize it with other global repositories.

Have a look at his lab page for more information: https://www.kyobinkanglab.org

Presentation

Computational Mass Spectrometry-based Qualitative Metabolomics for Functional Phenotyping of Xenobiotic Biotransformation

Structural annotation of unknown metabolites has long been considered the central bottleneck of untargeted, mass spectrometry-based metabolomics — a challenge that has persisted for over two decades. Recent advances in computational mass spectrometry, however, are dramatically improving annotation rates across diverse application domains. By decomposing complex metabolite mixtures into structural, substructural, and chemical class information, these tools enable not only the putative identification of key biomarker features, but also the generation of mechanistically grounded hypotheses directly from annotation data. Xenobiotic biotransformation represents a particularly compelling case study for discovery metabolomics: the enzymatic transformation of foreign compounds by human and microbial systems produces structurally diverse metabolite landscapes that are well-suited to computational annotation workflows. In this presentation, I will demonstrate how state-of-the-art computational MS approaches can accelerate functional characterization of xenobiotic biotransformation catalyzed by human and microbial enzymes.