<h3>Mnova 15.1 is Here!</h3>We’re excited to announce the release of <a target="_blank" rel="noopener noreferrer" href="https://bit.ly/4evoo5H">Mnova 15.1</a>, packed with new tools, enhanced features, and powerful algorithms designed to enhance your analytical workflows.&nbsp;Whether you're focused on NMR, LC/GC-MS or Electronic &amp; Vibrational Spectroscopic techniques, Mnova 15.1 has something to boost your analytical data analyses and streamline your workflows.<h4>What’s New in Mnova 15.1?</h4>Get a glimpse of the <a target="_blank" rel="noopener noreferrer" href="https://bit.ly/48InTUC">Top features in Mnova 15.1</a>Mnova 15.1 brings significant updates and improvements across our suite of plugins including Mnova NMR, <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/products/1077">MSChrom</a>, DB, Advanced Chemometrics, BioHOS, StereoFitter, <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1128">qNMR</a>, Structure Elucidation, IUPAC Name, NMRPredict, <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1081">Mgears</a>, <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1084">Affinity Screen</a>, <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1079">Chrom Best Method</a>, and <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1085">Reaction Optimization</a>. These updates introduce new functionalities, performance enhancements, and essential bug fixes to optimize your analytical workflows.For a detailed overview of all new enhancements and improvements check out our complete <a target="_blank" rel="noopener noreferrer" href="https://bit.ly/3O1mGhF">Changelog</a>.<h4>Ready to Upgrade?</h4>Don’t miss out on these cutting-edge innovations. Visit our website to <a target="_blank" rel="noopener noreferrer" href="https://bit.ly/4evoo5H">Download Mnova 15.1</a> and take your analytical workflows to the next level.<blockquote><h4>Watch showcase videos of a novel tool for Peak Purity Assessment</h4><ul><li><a target="_blank" rel="noopener noreferrer" href="https://youtu.be/FkJ43lQrMwI">Our solution for LCMS and GCMS included in Mnova MSChrom</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://youtu.be/nab8pR2RAbE">Our solution for chemometrics included in Mnova Advanced Chemometrics and Mnova BioHOS</a></li></ul></blockquote>

What's New in Mnova 15.1?

Fragment-Based Drug Discovery (FBDD) and validation of small molecules binders using NMR has emerged as a powerful method in early-stage drug development. This post explores the general use of analytical techniques and innovative screening software solutions that optimize the process, enabling researchers to identify promising drug candidates with precision.<h4>Analytical Techniques in Fragment-Based Drug Discovery</h4>To kickstart the FBDD journey, researchers employ ligand- or protein-observed NMR methods to detect binders from a library of small compounds. These analytical techniques, such as Nuclear Magnetic Resonance (NMR) spectroscopy, provide valuable data that play a crucial role in structurally characterizing potential drug molecules.<h4>Efficient Screening Tools for Hit Finding</h4>In the field of FBDD, researchers can benefit from a suite of powerful screening tools, including Mnova Screen 1D and <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1597">Mnova Screen 2D</a>, designed to efficiently analyze both 1D and 2D NMR data. These tools facilitate the analysis of 1D 1H or 19F screening data, including STD, T1ρ, WaterLogsy, and CPMG methods. Additionally, they allow batch processing of protein-observed 2D NMR spectra, such as 1H-15N or 1H-13C SOFAST HMQC. Automated peak picking and Q-Score ranking significantly expedite the identification and ranking of potential binding hits, empowering researchers to make data-driven decisions.<h4>Measuring Binding Affinities with Precision</h4>For researchers exploring multiple titration series, another piece of software, <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1592">Mnova Binding</a>, offers seamless batch processing of 2D NMR titration data. By fitting chemical shift perturbations (CSP) along titration curves, this tool accurately measures binding affinities (KD). Additionally, it automatically selects and averages individual KD values, facilitating comprehensive analysis.<h4>Unified Software Solutions for FBDD</h4>As illustrated in Fig 1, by presenting these software solutions, including Mnova Screen 1D, Mnova Screen 2D, and Mnova Binding, in a cohesive manner, researchers can benefit from a comprehensive toolbox with the same user-friendly interface to maximize the impact of NMR data, paving the way for innovative drug discoveries.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1_Illustration_of_fragment_based_drug_discovery_using_1_D_or_2_D_NMR_and_the_Mnova_software_tools_for_analyzing_such_data_c6b313ee88.jpg" alt="Mestrelab Research:Figure 1. Illustration of fragment-based drug discovery using 1D or 2D NMR, and the Mnova software tools for analyzing such data: Screen for 1D ligand-observed data such as STD, T1r, WaterLogsy and CPMG; Screen 2D for 2D protein-observed data such 1H-15N or 1H-13C SOFAST HMQC; Binding for titration series using 2D 1H-15N, 1H-13C, or 1H-13C/15N dual SOFAST-HMQC etc." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1_Illustration_of_fragment_based_drug_discovery_using_1_D_or_2_D_NMR_and_the_Mnova_software_tools_for_analyzing_such_data_c6b313ee88.jpg 245w," sizes="100vw" width="1600" height="686"><h4>Conclusion</h4>In conclusion, this post highlights the significance of Innovative screening software solutions in advancing Fragment-Based Drug Discovery by NMR. By exploring the power of these screening tools, researchers gain a deeper understanding of potential drug molecules, accelerating the drug development process. Let’s embrace the potential of software solutions to revolutionize FBDD by NMR, uncovering new therapeutic possibilities for the future.<a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/contact">Contact us</a> if you would like to talk to one of our experts about these NMR screening tools.You can also watch a demonstration of a related application in one of our webinars held in October 2023. “<a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/webinars/fragment-based-drug-discovery-application-webinar.html">Efficiently Driving Protein-based NMR Fragment Screening and Lead Discovery</a>”

Optimizing Fragment-Based Drug Discovery by NMR: A Deep Dive into Mnova Screening Software Solutions

<h4>The Role of Computers in Structural Problem Resolution:</h4>The use of computers is not just helpful but often essential in resolving structural problems. Computer-based structural elucidation (CASE) such as <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1599">Mnova Structure Elucidation</a> proves invaluable, particularly in navigating challenges posed by molecules with a poor hydrogen to carbon ratio. Computational techniques become increasingly relevant when determining stereochemical configuration and conformation.<h4>Steps in Computational Structural Elucidation:</h4><ul><li>Generation of Stereoisomers: Automation alleviates the tedium associated with generating diastereoisomers.</li><li>Conformational Search: Molecular mechanics force-fields aid in exploring conformational space.</li><li>Structural Refinement: Density functional theory (DFT) geometry optimization refines structural accuracy.</li><li>NMR Properties Computation/Analysis: Various NMR observables contribute to structural analysis.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_A_Symphony_of_NMR_Data_Enhancing_Structural_Elucidation_080945b762.jpg" alt="Mestrelab Research: A Symphony of NMR Data: Enhancing Structural Elucidation" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_A_Symphony_of_NMR_Data_Enhancing_Structural_Elucidation_080945b762.jpg 245w," sizes="100vw" width="936" height="559"><h4>Employing Different NMR Observables:</h4>Utilizing Different NMR Observables: The application of various NMR observables adds a subtle dimension to structural analysis, allowing for a comprehensive understanding of molecular characteristics. Each observable contributes unique insights, enhancing the precision and reliability of the elucidation process.<h5>Beyond the Use of NOE Effects:</h5>While NOE effects traditionally serve a qualitative role in structural analysis, their potential goes beyond. Rigorous analysis of kinetic NOESY/ROESY 1D or 2D experiments, following well-described protocols, yields accurate internuclear proton-proton distances. This data proves exceptionally effective in conformational analysis, particularly in complex medicinal chemistry problems. (1)<h5>J-Couplings and Karplus-like Relationships:</h5>Vicinal J-couplings provide essential dihedral angle information, crucial for stereochemical analysis. Empirical Karplus-like relationships allow quantitative use aspect of this information. Additionally, DFT predictions offer not only more accurate data but also the flexibility to incorporate various couplings, such as geminal 1J(C-H), 2J(C-H) couplings, or even the possibility to establish Karplus relationships based on DFT analysis of model systems. <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/resources/stereofitter-how-to-use-j-couplings.html">How to use J-couplings</a>.<h5>Chemical Shift Analysis Supported by DFT Predictions:</h5>Chemical shift analysis of 13C and 1H data, supported by DFT computations, has become increasingly popular for structural elucidation of natural products. This method proves valuable not only for de novo structural determination but also for the revision of already reported compounds.<h5>Residual Dipolar Couplings (RDC):</h5>RDCs emerge as a formidable tool for establishing configuration and conformation due to their capability to correlate the configuration of far away stereocenters. (2) &nbsp;They are applicable to a spectrum of structural problems, ranging from classical natural product elucidation, to &nbsp;medicinal chemistry applications such as the conformation guided design of cyclic peptides. (3) Weakly-aligning media, such as different types of liquid crystals or polymer gels, provides a user-friendly approach to the measurement of RDCs. (4)In summary, the strategic incorporation of these diverse NMR observables enhances the analytical arsenal, enabling a thorough and efficient exploration of structural complexities. Mestrelab’s commitment to providing tools like <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1598">Mnova StereoFitter</a> empowers users to seamlessly integrate and analyze this wealth of NMR and structural data for conclusive structural elucidation.You can watch a demonstration of a related application in one of our webinars held in November 2023. &nbsp;“<a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/webinars/webinar-pharmaceutical-molecule-characterization-and-structure-analysis.html">Advanced NMR Strategies for Pharmaceutical Molecule Characterization and Structure Analysis</a>”

A Symphony of NMR Data: Enhancing Structural Elucidation

Play FID is a new tool included within the latest version of <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1081">Mnova</a> 15.0.1. The purpose of Play FID is to provide an auditory representation of NMR data, enhancing the interpretation and understanding of NMR spectra. This feature is especially useful in educational settings and for aiding the identification of specific functional groups in a spectrum. It introduces a unique approach by adding an auditory dimension to the typically visual analysis of NMR data.Discover the innovative intersection of sound and science in our recently published article on auditory NMR spectroscopy, “<a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/articles/molecular-melodies-unraveling-the-hidden-harmonies-of-nmr-spectroscopy.html">Molecular Melodies: Unraveling the Hidden Harmonies of NMR Spectroscopy</a>“<blockquote>Iria Pérez: "As an engineer, I always aspired to make significant contributions to science. Little did I know that my work would one day enable us to hear molecules. I believe in the immersive potential of Play FID for scientifical research, education and art, since even I enriched my knowledge about NMR through my involvement with Play FID. I’m grateful to have worked with a highly talented team, who even created some unique compositions I had the pleasure enjoy. I look forward to seeing how this feature will be explored in many fields…”Gavin Shear: “When I worked for another NMR software provider, by far the most requested feature I got from students, was the one from MestReC that played the FID as a sound. To see it come back to Mnova is glorious, I’m over the moon to be able to hear what my acquisitions sound like again – and even better than before – I can save the sounds and use them in compositions!…”Carlos Cobas: “More than 20 years ago, I introduced the capability to play NMR FIDs in MestReC. Ever since, I’ve been eager to extend this feature to Mnova, along with implementing long-considered enhancements. Now, I’m delighted to finally have the opportunity to bring this to fruition with the support of an exceptional team…”</blockquote>

Play FID

<h4>Introduction</h4>Since the very first release of <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1081">Mnova</a>, we have been (and still are!) very fortunate to include in the software the prediction of NMR spectra provided by Modgraph Consultants. It is really a privilege to offer prediction capabilities developed by the pioneers and leaders in the field for so many years. Nowadays, when Machine and Deep Learning techniques are so popular, it is worth remembering that the predictions commercialized by Modgraph have used Neural Networks (in addition to other methods, vide infra) for more than 25 years already.Modgraph pioneered the concept of combining several NMR prediction methods. For example, in the case of 13C Prediction, Modgraph combines a Neural Network prediction with the so-called <a target="_blank" rel="noopener noreferrer" href="http://www.modgraph.co.uk/product_nmr_HOSE.htm">HOSE prediction</a>, both developed by Professor Wolfgang Robien of the University of Vienna and recognized as to offer the most reliable C13 NMR predictions for many years. They have implemented a procedure that selects the “Best” C13 prediction for each atom from the two prediction methods (Neural Network + HOSE).Based on the idea that different predictors can be combined to form a potentially better predictor, we have added more predictors since Mnova version 14 which are combined (using a Bayesian approach) with two main objectives: (1) Improve overall prediction accuracy (2) Reduce the number of prediction outliers.<h4>The need of an ensemble NMR Prediction</h4>Leaving aside slow quantum mechanical (QM) calculations, usually by the gauge-invariant atomic orbital (GIAO), all fast NMR prediction methods use in one form or other databases of assigned data. In a nutshell, we can consider the following fast prediction approaches:<ul><li>Increments methods (look-up tables of fragments)</li><li>HOSE-code methods</li><li>Machine Learning methods</li></ul>The first two methods perform quite accurately as long as the predicted atom is well represented in the internal data base.However, no matter how large the database used, it is going to be extremely tiny compared to the actual chemical space (i.e. the universe of possible compounds). For example, there are more than 166 billion organic molecules up to 17 atoms of C, N, O, S, and halogens, sizes compatible with many drugs [1]. And if we consider larger molecules (e.g. MW up to 500), the best guess for the number of plausible compounds is around 1060. There is therefore an effectively endless frontier.On the other hand, whilst Machine Learning (ML) methods are known to show a higher extrapolation/interpolation power, the accuracy of the prediction will nevertheless be compromised to some extent by the similarity of the chemical environment of the atom to be predicted as compared to the data set used to train the ML model.In summary, the accuracy of these fast NMR predictors depends, to a greater or lesser extent, on the contents of the assigned database. And whilst there are very large databases such as those provided by Modgraph 13C NMRPredict, it will be impossible to cover the full chemical universe.But what if different prediction algorithms, trained with different data sets, are used together? In principle, one might expect that any deficiency of one of the available predictors can be compensated by any of the other predictors. This could help not only to get more accurate predictions but also to reduce the number of prediction outliers where the predicted values were exceptionally poor for a particular individual predictor.Actually, in the field of Machine Learning, the concept of combining multiple learning algorithms &nbsp;(e.g. classifiers) is quite popular and it is known as Ensemble Learning.<h4>Ensemble NMR Prediction</h4>Suppose that a chemical shift has been estimated by several ‘predictors’ and that each estimator can characterize in some way the ‘reliability’ of its own estimate (i.e. prediction confidence interval).How should one combine the various partial predictions and derive the statistical characteristics of the final outcome? This is a wide area of study, as there are many different special situations (contexts). A very naïve approach would consist in simply averaging the values. A weighted average using the confidence interval of each predictor would be probably a better approach. We have implemented a Bayesian mechanism that uses the information of the individual predicted chemical shifts (such as their distances) as well as their corresponding confidence intervals. A detailed description of the method is beyond the scope of this entry, but will be published elsewhere later.The overall idea is illustrated in the figure below. Mestrelab and Modgraph predictions are executed in the background. Each predictor gives both chemical shifts (δ) and an estimate of its reliability (σ). Next, those individual values are combined together to yield a final chemical shift and reliability.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1_Combination_of_several_individual_predictors_to_yield_a_final_prediction_7cd81fd8ca.jpg" alt="Mestrelab Research: Figure 1 - Combination of several individual predictors to yield a final prediction" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1_Combination_of_several_individual_predictors_to_yield_a_final_prediction_7cd81fd8ca.jpg 245w," sizes="100vw" width="895" height="380">In practice, this is how it works in Mnova. First of all, it is necessary to check the 1H or 13C Prediction options to ensure that different predictions are available. This might depend upon the licenses installed, but assuming that they are available, the options should look like this:<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_2_13_C_Prediction_options_in_Mnova_14_f3196b0f82.jpg" alt="Mestrelab Research: Figure 2 - 13C Prediction options in Mnova 14" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_2_13_C_Prediction_options_in_Mnova_14_f3196b0f82.jpg 182w," sizes="100vw" width="354" height="304">For the moment, Modgraph NMRPredict Server can be ignored. If the other two options are checked, when a prediction is issued, Mnova runs several predictors in the background. First, Mestrelab Predictor, which in fact, is formed by two different Machine Learning predictors (and both trained with different assigned data). Next, Modgraph Predictor is also executed (which in turn, uses two different predictors, vide supra). Finally, Mnova uses the Bayesian algorithm to combine all the individual chemical shifts and confidence intervals to yield the final predicted chemical shifts and confidence intervals.As a result, Mnova will show the unified, final results, but it is possible to visualize the individual values for each predictor by inspecting the Mnova log file, although this will not be covered in this post.<h4>Some results</h4>To illustrate the benefits offered by the combination of several predictors a simple exercise has been conducted. A small set of 13 assigned molecules abstracted from the literature has been used. This was done by an internship student who had no knowledge about the contents of the Mnova prediction assigned DBs. In principle, the ideal set of molecules should be those that were not contained within the internal prediction DBs or used in the Machine Learning training sets. However, it was found that these molecules are actually pretty well represented in the internal DBs, as shown in Table 1. It can be seen that most of the Carbon atoms can be described by 4 or 5 shells. This is not a coincidence: it confirms the high quality (and huge size) of the Modgraph’s 13C HOSE DB!<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Table_1_Distribution_of_number_of_shells_for_the_molecules_used_in_this_test_9ac66b645e.jpg" alt="Mestrelab Research: Table 1 - Distribution of number of shells for the molecules used in this test." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Table_1_Distribution_of_number_of_shells_for_the_molecules_used_in_this_test_9ac66b645e.jpg 245w," sizes="100vw" width="252" height="125"> Nevertheless, bearing this point in mind, this small set of molecules should be good enough for this simple exercise. The overall results are summarized in Table 2.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Table_2_Mean_absolute_errors_for_the_individual_and_ensemble_predictor_f01a1974d9.jpg" alt="Mestrelab Research: Table 2 - Mean absolute errors for the individual and ensemble predictor" width="194" height="100"> First, it can be observed that the Mean Absolute Error (MAE) of Modgraph is remarkably low (1.63 ppm), reflecting the previous point that showed that most of the atoms in the test set are represented by 4 or 5 shells. The other two predictors (Machine Learning 1 and Machine Learning 2) also yield very good results, although slightly worse than Modgraph.The question is: can those 3 predictors be combined in such a way that the result is better than all the individual predictors? That is the goal of the ensemble predictor and, as it is shown in Table 1, this is indeed the case: the ensemble predictor has a MAE = 1.3, a value which, in the context of 13C NMR prediction can be considered as exceptionally good.Somehow, the ensemble predictor attempts to compensate any deficiencies of any of the individual predictors. For this to work, it is crucial that each predictor provides a reliable confidence interval and a very significant effort has been put into that. Additional insights can be extracted by analyzing the histograms of the prediction errors for the individual and final ensemble predictors. They are shown in Table 3 and Figure 3.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Table_3_Distribution_of_prediction_errors_for_the_different_individual_predictors_as_well_as_for_the_final_ensemble_result_4107e5f4d6.jpg" alt="Mestrelab Research: Table 3 - Distribution of prediction errors for the different individual predictors as well as for the final, ensemble result." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Table_3_Distribution_of_prediction_errors_for_the_different_individual_predictors_as_well_as_for_the_final_ensemble_result_4107e5f4d6.jpg 239w," sizes="100vw" width="1321" height="862"><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_3_Histrogram_plot_corresponding_to_the_data_shown_in_Table_3_b7511c7058.jpg" alt="Mestrelab Research: Figure 3 - Histrogram plot corresponding to the data shown in Table 3" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_3_Histrogram_plot_corresponding_to_the_data_shown_in_Table_3_b7511c7058.jpg 245w," sizes="100vw" width="342" height="207"> The most obvious conclusion that can be drawn from these histograms is that the ensemble method helps to group the prediction error around the low MAE zone and hence, reduce the number of large prediction errors. In other words, this method assigns a higher weight in the final prediction result to that prediction with a smaller confidence interval. However, it is important to bear in mind that this is an oversimplification as the Bayesian algorithm not only uses the confidence interval but also the distance in the predicted chemical shifts amongst the different predictors.To complement this article, &nbsp;you can see some some results for 1H NMR data in the this blog post where a few assigned molecules were taken from the literature.

Ensemble NMR Prediction

With the release of <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/main-product/mnova?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=june&amp;utm_term=mnova%2016&amp;utm_content=blogpost">Mnova 16</a>, a new and powerful way to handle results from scientific data analysis is now available by using the NoSQL DB Connector, included in <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/main-product/gears?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=july&amp;utm_term=mnova%2016%20-%20nosql&amp;utm_content=app%20note">Mgears 2.7</a>.This new connector enables direct integration with NoSQL databases such as MongoDB and Amazon DocumentDB, allowing for flexible, scalable, and searchable storage of Mgears results – perfect for high-throughput environments and complex workflows.A newly published Application Note explores:<ul><li>When and why connecting Mgears to a NoSQL DB is beneficial</li><li>How to send results from Mgears to a NoSQL DB</li><li>How to build filtered views to show selected sample data</li><li>How to create simple views for use in BI tools like Spotfire</li><li>How data stored in NoSQL DB can be connected to AI frameworks like TensorFlow for building predictive models</li></ul>These capabilities are illustrated with real examples using datasets from LCMS system suitability testing and reaction optimization analysis, demonstrating how this connector offers the capability to handle analytical data in a more dynamic way resulting in faster data-driven decision-making.The connector works with Mgears’ native JSON outputs and avoids the need for predefined schemas – making it especially suitable for workflows that evolve over time.<blockquote><a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/resources/the-mgears-nosql-db-connector.html?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=july&amp;utm_term=mnova%2016%20-%20nosql&amp;utm_content=app%20note">Read the full Application Note.</a></blockquote>

Mgears 2.7 Introduces a new NoSQL DB Connector

<h4>A New Release Supporting Evolving Analytical Workflows</h4>SciY is pleased to announce the release of Mnova 16, the latest version of one of the most widely adopted analytical software platforms in the scientific community. Introduced at the end of May 2025, Mnova 16 delivers a focused set of enhancements that responds to the growing need for structured, interoperable, and automation-ready data workflows across scientific and industrial laboratories.Among the most notable improvements are expanded Python scripting capabilities that offer greater flexibility for users to automate tasks and tailor Mnova to their specific workflows. Mnova 16 also brings native support for the Allotrope Simple Model (ASM), allowing NMR data to be stored in a standardized, metadata-rich format, supporting data interoperability and compatibility with modern digital ecosystems.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Sci_Y_Mnova_16_launch_New_Tools_for_Data_Integration_Automation_and_User_Control_a8bc19ad79.jpg" alt="SciY: Mnova 16 launch - New Tools for Data Integration, Automation, and User Control" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Sci_Y_Mnova_16_launch_New_Tools_for_Data_Integration_Automation_and_User_Control_a8bc19ad79.jpg 245w," sizes="100vw" width="1280" height="720">Additionally, Mnova 16 introduces a new Command Palette, enabling users to quickly access functions through a streamlined interface, improving usability and reducing time spent navigating menus. This release also includes updates to several key products — Mgears 2.7, Mnova Hub 1.1, Chrom Cal 1.1, and qNMR 3.3 — to provide new functionalities, improve integration, and enhance user experience.By delivering these targeted improvements, Mnova 16 helps users stay current with evolving standards and technologies, empowering them to innovate without limitations. For a detailed overview of the new features in Mnova 16, visit the <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/resources-by-product/resources-mnova/top-features-in-mnova-16.html">Mnova 16 – Top Features web post</a>.<h4>Mnova as Part of SciY: Supporting Structured, Scalable Scientific Workflows</h4>Now part of the SciY software offering, Mnova is increasingly aligned with a broader ecosystem of modular tools designed to support digitalization, automation, and AI readiness.This closer collaboration within the SciY portfolio has led to new integrations, such as Mnova Hub now connecting Mnova with both ZONTAL and LOGS (introduced in the Mnova 16 release), facilitating structured and centralized data management that goes beyond data analysis. This ecosystem is key to enabling lab-wide digitalization, improved traceability, and readiness for AI-driven applications.Through SciY, Mnova users can now access a growing set of interoperable tools, benefiting from improved compatibility, coordinated support, and faster innovation through shared development efforts.&nbsp;<blockquote><h5>About SciY</h5>SciY is the agnostic software division of Bruker, dedicated to advancing scientific software for digital transformation. SciY brings together a portfolio of modular, interoperable tools that support the entire analytical data lifecycle—from acquisition and processing to management, automation, and AI readiness. With a focus on open standards, vendor neutrality, and seamless integration, SciY empowers laboratories to modernize their operations and unlock the full value of their scientific data.</blockquote>

Mnova 16 launch - New Tools for Data Integration, Automation, and User Control

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