Dataset Information

Rapid prediction of NMR spectral properties with quantified uncertainty.

ABSTRACT: Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both [Formula: see text] and [Formula: see text] nuclei which exceeds DFT-accessible accuracy for [Formula: see text] and [Formula: see text] for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.

SUBMITTER: Jonas E

PROVIDER: S-EPMC6684566 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Rapid prediction of NMR spectral properties with quantified uncertainty.

Jonas Eric E Kuhn Stefan S

Journal of cheminformatics 20190806 1

Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both [Formula: see text] and [Formula: see text] nuclei which exceeds DFT-accessible accuracy for [Formula: see text] and [Formula: see text] for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allo ...[more]

PMID: 31388784

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Project description:Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

Dataset Information

Rapid prediction of NMR spectral properties with quantified uncertainty.

Publications

Rapid prediction of NMR spectral properties with quantified uncertainty.

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