Accurate Prediction of <sup>1</sup>H NMR Chemical Shifts of Small Molecules Using Machine Learning
2024
Tanvir Sajed | Zinat Sayeeda | Brian L. Lee | Mark Berjanskii | Fei Wang | Vasuk Gautam | David S. Wishart
NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict <sup>1</sup>H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict <sup>1</sup>H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict <sup>1</sup>H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.
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