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Webinar – Digitalize your chemistry lab with Mbook 3.0!

The webinar will cover the complete workflow required to create digital laboratory notes in Mbook 3.0. The outline is a high-level introduction, followed by a demonstration of typical daily workflow, and a Q&A session.

The new features of Mbook 3.0 will be covered together with the chemist’s daily workflow, as they are inseparable from the chemist’s work.

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NovAliX adopts Mestrelab’s Mnova software

Santiago de Compostela, Aug 2, 2021. The adoption of Mnova software comes on the heels of a major expansion of chemistry at NovAliX with the acquisition […]
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Mbook 3.0 – New features

Mbook 3.0 is out with a whole set of new features, making your ELN experience more complete and better suited to your needs. Here are the top eight new features and other new ones you can enjoy!

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QC Profiling 1.1 – User Manual

  Mnova Gears – QC Profiling USER MANUAL Automated LC-MS Quantitation of DNA Labeling Reactions QC Profiling is a brick or plugin of Mnova Gears. It […]
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Mnova MyGears – App Note

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

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Top 14 highlights in Mnova 14.2

Mnova 14.2 has just been launched as an intermediate release version with a high number of new features for most of our products and the usual bunch of bug fixes, as well as some plugin updates. You can check them all out on our changelog page. Here, we would like to highlight what we consider as the top ones for our customers.

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QC Profiling – Starting guide

QC Profiling is a new brick or plugin of Mnova Gears. It has been designed as an automated evaluation for the quality control of reactions used […]
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Mnova DB MyData – Workshop

Store, share and search your chemical and analytical data

This is the recorded session of the workshop Mestrelab given by Mestrelab CEO, Santi Dominguez in April 2020.

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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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