Mnova Verify

Automatic Confirmation of Structure Identity Based on NMR 
and/or LC/GC-MS data

About Mnova Verify


Mnova Verify is a off-the-shelf solution designed for synthetic and medicinal chemists to automatically confirm molecular structures are compatible with a set of analytical data. By combining NMR (¹H, ¹³C, HSQC) and LC/GC-MS analyses, automatically analysing and interpreting the data and comparing it with a provided molecular structure.

Multi-Technique
Verification
Integrate NMR and LC/GC-MS data to validate chemical structures quickly and reliably
Intelligent Scoring System
Rank proposed structures by compatibility with experimental data using a numerical score
Automated, Scalable Analysis
Run fully automated verification on single samples or large datasets to save expert time
Real-Time & Batch Operation
Analyze data instantly after acquisition or run batch verification post-processing for maximum workflow flexibility

Benefits

Faster Turnaround: Mnova Verify dramatically reduces the time spent on manual verification for every sample by automatically processing data, comparing it to the proposed structure, and delivering an instant pass/fail outcome.

Higher Throughput: Automation of batch and real-time workflows increases output without sacrificing quality, enabling quicker decisions and clear go/no-go calls.

Fewer Misassignments: By applying scoring and consistency checks, Mnova Verify reduces the risk of incorrect structure identification before downstream assays or submissions.

Clarity for Everyone: Mnova Verify presents results visually and clearly to provide easy-to-interpret outputs so non-experts can make confident decisions.

Publications

  1. Cobas, C. NMR signal processing, prediction, and structure verification with machine learning techniques. Magnetic Resonance in Chemistry 58, 512-519 (2020). https://doi.org/10.1002/mrc.4989
  2. Lumley, J. A., Sharman, G., Wilkin, T., Hirst, M., Cobas, C., Goebel, M. A KNIME Workflow for Automated Structure Verification. SLAS Discovery 25, 950-956 (2020). https://doi.org/10.1177/2472555220907091

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