一种预测醇化学位移的新方法(英文)-豆柴文库

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一种预测醇化学位移的新方法(英文)
Title:ANovelApproachforPredictingAlcoholicChemicalShifts
Abstract:
Chemicalshiftpredictionisacriticalaspectofnuclearmagneticresonance(NMR)spectroscopy,particularlyinthefieldoforganicchemistry.Accuratepredictionsofchemicalshiftscanaidintheidentificationandcharacterizationofcompounds,facilitatingelucidationoftheirstructuresandproperties.Inthispaper,wepresentanovelmethodforpredictingalcoholicchemicalshifts,whichemploysmachinelearningalgorithmstrainedonacomprehensivedatasetofexperimentalNMRspectra.Ourapproachaimstoimprovetheaccuracyandefficiencyofchemicalshiftpredictions,offeringsignificantadvantagesoverexistingmethods.
Introduction:
Chemicalshiftsarevaluableindicatorsofmolecularstructureandenvironment.Inthecaseofalcoholiccompounds,thechemicalshiftoftheprotonattachedtothealcoholfunctionalgroupisofparticularinterest.Accuratepredictionsofalcoholicchemicalshiftscanassistindeterminingthenatureandconfigurationofalcohols,enablingresearcherstomakeinformeddecisionsduringsyntheticandanalyticalprocesses.
Existingmethodsforpredictingchemicalshiftstypicallyrelyonempiricalrules,suchaschemicalshiftincrementsoradditivecorrectionsbasedonfunctionalgroups.Whilethesemethodshaveprovidedusefulapproximations,theyoftenlackaccuracy,especiallywhendealingwithcomplexanddiversechemicalsystems.Additionally,empiricalrulesmaynoteffectivelycapturesubtlechangesinchemicalenvironment,limitingtheirpredictivepower.Inthisstudy,weproposeamachinelearning-basedapproachtoaddresstheselimitationsandimprovethepredictionofalcoholicchemicalshifts.
Methodology:
Todevelopourpredictionmodel,weutilizedadiversedatasetcontainingexperimentalNMRspectraofalcoholiccompoundswithknownchemicalshifts.Thisdatasetwascarefullycuratedandaugmentedtoensurerepresentationacrossvariousstructuralfeaturesandsolventenvironments.Thespectrawerepreprocessedtoextractrelevantfeatures,suchaspeakintegrals,couplingconstants,andotherspectralparameters.
Weemployedseveralmachinelearningalgorithms,includingrandomforests,supportvectorma