ArticleChemical science2018
Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
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- New 1,3,4-Thiadiazole Derivatives as α-Glucosidase Inhibitors: Design, Synthesis, DFT, ADME, and In Vitro Enzymatic Studies.ACS omega · 2024Article
- Materials informatics approach using domain modelling for exploring structure-property relationships of polymers.Scientific reports · 2022Article
- Real-time prediction ofChemical science · 2021Article
- The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.RSC advances · 2021Review
- Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN).ACS omega · 2021Article
- Relaxometric learning: a pattern recognition method for TBMC chemistry · 2021Article
- Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials.International journal of molecular sciences · 2021Article
- NMR-TS: de novo molecule identification from NMR spectra.Science and technology of advanced materials · 2020Article
- Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems.Communications chemistry · 2020Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Various chemical shift predictive methodologies have been studied and developed, but there remains the problem of prediction accuracy. Assigning the NMR signals of metabolic mixtures requires high predictive performance owing to the complexity of the signals. Here we propose a new predictive tool that combines quantum chemistry and machine learning. A scaling factor as the objective variable to correct the errors of 2355 theoretical chemical shifts was optimized by exploring 91 machine learning algorithms and using the partial structure of 150 compounds as explanatory variables. The optimal predictive model gave RMSDs between experimental and predicted chemical shifts of 0.2177 ppm for
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