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Skinnider lab develops machine-learning approaches to identify known unknown small molecules relevant to human health and disease, utilizing mass spectrometry-based metabolomics as the primary analytical technique. The human body contains thousands of small molecules, which are encountered daily. This complex chemical ecosystem reflects endogenous metabolism, xenobiotic exposures, diets, gut flora, and built environments. Collectively, small molecules influence disease risk, determine responses to prescription drugs, and provide molecular biomarkers used in clinical diagnostics and treatment selection. Currently, however, the vast majority of small molecules remain unknown. High-throughput techniques can reliably measure DNA, RNA, and protein content in biospecimens, but enumerating the complete complement of small molecules—the metabolome—has proven challenging. Mass spectrometry (MS), a workhorse of metabolomics, can detect thousands of molecules in routine experiments, yet the majority cannot be definitively identified. This profusion of unidentified chemical entities has been dubbed the 'dark matter' of the metabolome. The lab is focused on illuminating this dark matter by developing new computational approaches to identify these known unknown small molecules using mass spectrometry. To achieve this aim, the lab designs and applies cutting-edge AI technologies to translate mass spectrometric information into chemical structures, with particular emphasis on the role of unknown metabolites in cancer and connections to germline risk factors and the human microbiome.
Lewis-Sigler Institute • Princeton University
Leading research on machine-learning approaches for small molecule identification relevant to human health and disease.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.