January 21, 2020
Journal Article

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Abstract

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing

Revised: February 2, 2021 | Published: January 21, 2020

Citation

Colby S.M., J. Nunez, N.O. Hodas, C.D. Corley, and R.S. Renslow. 2020. Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples. Analytical Chemistry 92, no. 2:1720-1729. PNNL-SA-144150. doi:10.1021/acs.analchem.9b02348