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Automatic confirmation of structure identity based on NMR and/or LC/GC-MS data

Designed for synthetic and medicinal chemists, which automatically checks a proposed structure against a set of analytical data, LC-MS or GC-MS and/or 1D NMR and/or 2D NMR.


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Mnova NMR, NMRPredict, and MSChrom licenses are required for an optimal verification.

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Benefits

Solution coupling NMR (1H, 13C and HSQC) and MS techniques in a commercial off-the-shelf software.

Helps non-expert users to take decisions while reducing the workload of the experts.

Fully automatic analysis for singletons or batch data.

Mnova Verify checks whether the proposed compound is correct!

Flexibly accepts a variety of inputs and generate automatically fully customized reports.

Straightforward results check that warns about inconsistencies.

Verify in automation

Gears Verify

Integrate your verification analyses to a fully automated workflow. Quick and efficient analysis of data acquired in Real Time or in large Batches and generates automatic reports that will save you huge amounts of time!

Go to Gears Verify
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Features

Powerful algorithm running behind the scenes

An easy and flexible scoring system

Automated batch & real-time operations

Latest Verify news

Mnova MyGears – App Note

MyGears in action – Automated qNMR, Structure Verification and Spectrum Suitability Testing with ONE Click.

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Mestrelab peer reviewed papers

A KNIME Workflow for Automated Structure Verification

Adequate characterization of chemical entities made for biological screening in the drug discovery context is critical. Incorrectly characterized structures lead to mistakes in the interpretation of structure–activity relationships and confuse an already multidimensional optimization problem.

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Mestrelab peer reviewed papers

NMR signal processing, prediction and structure verification with Machine Learning techniques

Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power.

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Academic, Government & Industrial

Target Audience

Organic Chemists
Medicinal Chemists
QC Experts
Process Chemists
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