Skinnider lab

The Skinnider lab develops machine-learning approaches to identify known and unknown small molecules that are relevant to human health and disease, with mass spectrometry-based metabolomics being the primary analytical technique.

The human body contains thousands of small molecules, and is exposed to thousands more during daily life. At present, however, the vast majority of these small molecules remain unknown. Whereas high-throughput techniques can now reliably measure the DNA, RNA, and protein content of any given biospecimen, enumerating the complete complement of small molecules—the metabolome—has proven much more challenging. Mass spectrometry (MS), the workhorse of metabolomics, is capable of detecting thousands of molecules in routine experiments, but the vast majority of these cannot be definitively identified. This profusion of unidentified chemical entities has been dubbed the “dark matter” of the metabolome.

We are interested in illuminating this metabolic dark matter by developing new computational approaches to identify both known and unknown small molecules using mass spectrometry. To achieve this aim, we design and apply cutting-edge AI technologies to translate mass spectrometric information into chemical structures. Some of the key challenges we are interested in include:

• Structure elucidation: decoding unknown chemical structures from MS/MS data
• Metabolomics: developing new tools for data analysis to deal with the complexity of metabolomic experiments
• Forensic chemistry: identifying new illicit drugs of abuse from clinical and forensic mass spectrometry data
• Natural products: discovering new bacterial natural products with genomic and metabolomic datasets
• Low-data learning: developing machine learning approaches to learn from small chemical datasets

We are recruiting at all levels (undergraduates, post-baccs, PhD students, and postdocs). If you are interested in joining the lab, please see the join page.