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NMRPredict

Accurate prediction of 1H and 13C NMR spectra from a chemical structure.

Mnova NMR Predict calculates accurate and precise NMR chemical shifts using a novel procedure that combines several prediction engines in a constructive way. This method is called Ensemble NMR Prediction and uses several Machine Learning methods in combination with the well-knonwn Increments and HOSE-code algorithms developed by Modgraph Consultants. Prediction of chemical shifts of other nuclides is also available.

To complement the article about Ensemble NMR Prediction you can also read a blog post about 1H data here

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Easily combine and compare experimental and predicted data as part of your workflow.

Easily combine and compare experimental and predicted data as part of your workflow.

NMRPredict

Highlights

Make better decisions for your spectra faster!

 

Compute and display accurate chemical shifts for1H、13C and other nuclides(11B、15N、17O、19F、29Si、31P) as well as J(HH)、J(HF)、J(HP)、J(CF)and J(CP)

Predicted 1H-NMR spectra are synthesized using a rigorous quantum mechanism approach that takes into account strong coupling effects.

If the experimental spectrum is available, prediction will use the same experimental conditions (e.g. solvent and spectral properties: spectral width, spectrometer frequency, chemical shift reference, digital number of points, etc.)

Train your predictions by building your NMR databases from already assigned molecular structures.

Ability to drill-down user assigned1H and 13C experimental data.

Each predicted value is accompanied by its confidence interval.

NMRPredict

Academic, Government & Industrial

  •      ✯   Pharmaceutical, chemical and food industries and QC environments
  •      ✯   Research and NMR teaching in Academia
  •      ✯   Suitable for individual users, research groups as well as large institutions and companies
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NMRPredict

Contact us

    ✈:No. 377, Nanjing Road, Shibei District, Qingdao, Shandong

    ☏:0532-83818797 / 18561885100

    ✉:changzhu_ ji@tlwb.com.cn

          shuochao_dai@tlwb.com.cn

          support@tlwb.com.cn

 

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