We are excited to introduce our Mnova Purification Suite, meticulously crafted to offer flexible, modular solutions that address the critical aspects of chromatographic purification. Our suite comprises three modules, each tailored to cater to distinct purification needs:<h3>Optimizing Separation Conditions with Chrom Best Method:</h3>Discover the optimal separation conditions for your compound through automated LC-MS data processing, method scoring, and report generation. This streamlines method development, allowing you to pinpoint the most suitable conditions for your compounds, thus minimizing trial-and-error iterations.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Optimizing_Separation_Conditions_with_Chrom_Best_Method_dd7244eaf7.jpg" alt="Mestrelab Research: Optimizing Separation Conditions with Chrom Best Method" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Optimizing_Separation_Conditions_with_Chrom_Best_Method_dd7244eaf7.jpg 234w," sizes="100vw" width="900" height="600"><h3>Fraction Pooling Decision Support with Fraction Analysis:</h3>Quickly decide whether to pool collected fractions by automating the analysis, peak matching, purity calculation, and automated result reporting. Make informed decisions about fraction pooling to maximize compound yield and purity, ultimately accelerating your purification process.&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Fraction_Pooling_Decision_Support_with_Fraction_Analysis_f82f2cf27f.jpg" alt="Mestrelab Research: Fraction Pooling Decision Support with Fraction Analysis" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Fraction_Pooling_Decision_Support_with_Fraction_Analysis_f82f2cf27f.jpg 234w," sizes="100vw" width="900" height="600"><h3>Quality Control Checks with Chrom QC:</h3>Confirm your analyte’s identity and assess its purity with molecule match score, percentage purity, and concentration assessment. Streamline lot and batch release programs through automated data processing, analysis, and report generation, facilitating enhanced lab productivity.&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Quality_Control_Checks_with_Chrom_QC_802de36f23.jpg" alt="Mestrelab Research: Quality Control Checks with Chrom QC" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Quality_Control_Checks_with_Chrom_QC_802de36f23.jpg 234w," sizes="100vw" width="900" height="600">Whether you choose to implement these modules individually or seamlessly integrate them into an enterprise solution is entirely your prerogative. Flexibility is at the core of our philosophy, and we strive to ensure that our solutions align with your evolving needs.Like all our Mnova solutions, the Purification Suite is compatible with various lab systems regardless of the vendor, ensuring seamless integration and facilitating unified software platform data handling. Each module offers a suite of fully configurable settings that can be customized to align with your Standard Operating Procedures (SOPs) and preferences, thereby enhancing adaptability and efficiency. Furthermore, these solutions offer advanced reporting and visualization dashboards, providing an interactive interface for reviewing results. You can utilize controls and flags to identify samples requiring review, enabling you to focus on exceptions and recalculate results as necessary. And since we acknowledge that human insight is irreplaceable, should any adjustments be necessary, you have the freedom to manually override results.If you’re eager to explore our purification data workflows and automation solutions further, or if you have specific inquiries, please do not hesitate to reach us out! We are here to assist you every step of the way.

Introducing the Mnova Purification Suite: your solution for streamlined chromatography purifications

Spectroscopic techniques, particularly NMR, frequently encounter the challenge of overlapping peaks. Thus, the need to enhance the resolution of such spectra has been the subject of extensive research for decades. In NMR, the pursuit of higher resolution began with hardware advancements, notably the development of sophisticated shimming systems. These systems were designed to improve magnetic field homogeneity and stability, as well as the stability of the radio frequency (RF) sources used. However, these improvements are inherently limited by the natural linewidths of the resonance peaks of the samples, which are predominantly dictated by relaxation effects and molecular mobility. The latter is often insufficient to fully average out dipolar interactions and other inhomogeneities.With the advent of computers in NMR technology, digitized spectra became the norm, and a persistent drive emerged to enhance resolution post-acquisition through computational algorithms. Over the decades, numerous algorithmic strategies have been proposed, yet the consensus in the scientific community suggests that the potential for further enhancements still exists.In this context, we introduce “ElPulpo“, a novel resolution enhancement algorithm specifically designed for 1D NMR spectra. ElPulpo distinguishes itself by its computational agility and its streamlined approach, requiring only one key parameter: the ‘Reference Linewidth’. Notably, a good default value of this parameter can be pre-estimated by means of another algorithm that we have used for years and integrated with ElPulpo. &nbsp;Very importantly, ElPulpo was meticulously designed with the following requirements in mind:<ul><li>It respects the intrinsic quantitative nature of NMR spectra, upholding its compatibility with quantitative NMR (qNMR) standards. Regardless of how ElPulpo affects the lineshapes of the spectral peaks, it does not alter their integrals —both absolute and relative—affirming its suitability for qNMR analysis. &nbsp;</li><li>ElPulpo works in the frequency domain, applying a number of evaluation steps. All these steps are rigorously (mathematically) linear and therefore fully compatible with NMR spectra of mixtures, no matter how complicated.</li><li>ElPulpo keeps under control the propagation of experimental noise. In mode 1 (see below) it effectively attenuates noise by approximately 50%. In modes 2 and 3, the noise propagation factor is around 0.8 and 1.2, respectively. Even with the higher modes that most aggressively reduce the linewidths of spectral peaks, the noise propagation factor is kept below approximately 3.5.</li><li>ElPulpo operates both on the real and imaginary parts of the spectrum and, by its mathematical nature, maintains the Hilbert-transform relationship between them. In practice, this means that it is fully compatible with spectral phasing practices, both manual and automatic. Spectra can be phased or re-phased either before or after the application of ElPulpo, a property that is often very handy.&nbsp; This document will not delve into the mathematical intricacies of ElPulpo; instead, it will focus on its practical application, demonstrating its effectiveness through various examples. &nbsp;</li></ul><h3>ElPulpo</h3>ElPulpo is available through the Mnova command Processing/More Processing…/ElPulpo Resolution Enhancement and requires two fundamental parameters:&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_El_Pulpo_e9449d951a.jpg" alt="Mestrelab Research: ElPulpo Resolution Enhancement" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_El_Pulpo_e9449d951a.jpg 193w," sizes="100vw" width="336" height="271"><h4>Resolution Mode</h4>This parameter balances resolution enhancement against noise effects in the algorithm’s operation. Lower modes (e.g., 1, 2) prioritize signal integrity, improving resolution conservatively while minimizing noise propagation. They are preferred when maintaining a high signal-to-noise ratio (SNR) is important.Higher modes (e.g. 4, 5) lean towards more aggressive resolution enhancement, suited for scenarios where resolution is paramount, albeit with decreased noise tolerance. This trade-off allows users to adapt the algorithm’s performance to specific analytical needs. The default value is mode 2.The figure below illustrates the impact of employing two distinct modes. On the left side, the original, unprocessed spectrum is displayed (cyan curve), overlaid with the spectrum processed by ElPulpo in mode 2 (red line). On the right side, the same spectrum is presented, this time processed using mode 5. It’s important to note that ElPulpo is engineered to preserve peak integrals; as it narrows line widths for enhanced resolution, it correspondingly increases peak heights to maintain integral constancy.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_El_Pulpo_Resolution_Mode_0508b6ef55.jpg" alt="Mestrelab Research: ElPulpo - Resolution Mode" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_El_Pulpo_Resolution_Mode_0508b6ef55.jpg 245w," sizes="100vw" width="1253" height="439"><h4>Reference Line Width</h4>This parameter is pivotal for the algorithm’s performance. The ideal value for the Reference Line Width should match the actual line width of the peaks in the spectrum. However, given the variability in peak widths across a spectrum, the algorithm requires a compromise value. The software facilitates this by offering an automatic setting for this parameter. It calculates the average line width of all peaks in the spectrum and then adjusts this average by a factor, currently set to 0.8. &nbsp;It is important to understand the behavior of the algorithm in response to adjustments in the Reference Line Width (refLW): When refLW is small, its influence on the peak diminishes, tending towards zero, minimally affecting the peak. Conversely, increasing refLW initially leads to peak narrowing, but exceeding a certain point introduces negative side lobes.Optimal peak refinement occurs when refLW matches the original line width (originalLW) of the peaks, avoiding negative lobes. If refLW is larger than originalLW, further narrowing of the central peak is accompanied by the emergence of negative lobes. On the other hand, when refLW is less than originalLW, narrowing diminishes and no lobes are formed.The figure below demonstrates the influence of different Reference Line Width settings in ElPulpo processing. Again, the cyan curve depicts the original spectrum, while the red curve represents the spectrum processed by ElPulpo. On the left, a Reference Line Width of 0.94, approximating the actual linewidth of the spectrum, is used. On the right, a slightly larger Reference Line Width of 1.14 Hz is applied. This adjustment leads to some excursions of the peaks into the negative domain, a direct consequence of the increased line width parameter.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_El_Pulpo_Reference_Line_Width_9ae1efe0c8.jpg" alt="Mestrelab Research: ElPulpo - Reference Line Width" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_El_Pulpo_Reference_Line_Width_9ae1efe0c8.jpg 245w," sizes="100vw" width="1259" height="439"><h3>Why ‘El Pulpo’? The Story Behind the Name:</h3>The reason why we have called the algorithm ElPulpo is that we imagine it as an octopus (but one with 13 tentacles) that crawls above the spectrum and, at each point, spreads its tentacles far enough (compared to the reference linewidths) to 13 different locations in the spectrum. It friendly ‘palpates’ the spectrum around those points and, using a separate multi-point interpolation algorithm, assesses the value of the complex spectrum at those pre-selected complex points. After that, using a composite Savitsky-Golay convolution filter, it uses the 13 values to extract a desired, theoretically determined combination of the original spectrum (derivative 0) with its higher derivatives (from 0 to M, where M is the ‘mode’ number), and then uses the computed value to replace the one of the point at which it has started.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_El_Pulpo_a_q_NMR_friendly_resolution_enhancement_algorithm_86d9924342.jpg" alt="Mestrelab Research: ElPulpo - a qNMR friendly resolution enhancement algorithm" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_El_Pulpo_a_q_NMR_friendly_resolution_enhancement_algorithm_86d9924342.jpg 245w," sizes="100vw" width="1200" height="572">The spreading of the octopus tentacles before getting a feel of the spectrum is essential to make the method (and especially its noise propagation factor) little sensitive to spectrum digitization that may range from quite poor (common for narrow peaks in NMR spectroscopy) to very dense, even magnitudes higher than necessary.&nbsp;

ElPulpo: a qNMR friendly resolution enhancement algorithm

In industries with discovery departments and purification processes, preparative chromatography is key for obtaining high-purity materials. But screening sample sets and analyzing results manually can be time-consuming. In this<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/LC_separation_procedures_for_Ezetimibe_App_Note_fccfab2a32.pdf"> application note</a>, Prof. Rafael Cela, Senior Researcher at the Mestrelab Research Center (CIM), showcases the selection of LC procedures using <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1079">Mgears Chrom Best Method </a>and highlights how automation streamlines the screening process and helps in analysis, reporting, and decision-making.Selected sections from the application note follows.Screening sample sets are common tasks in industries with discovery departments, and purification and scaling fabrication processes for high added value chemicals. In all these fields, preparative chromatography is often the preferred way to obtain material with sufficient purity. The efficient purification methodology involves screening a few chromatography methods using mass and/or UV detectors and testing different columns and/or mobile phase compositions.&nbsp;Frequently, these sample sets consider chiral compounds, and the objective is not only the separation of the target species such that it is as pure as possible, but also the separation of enantiomers and/or diasteroisomeric impurities. From this screen, critical decisions are taken (e.g., a synthetic route is preferred; a preparative method is chosen; alternative separation methods are tested; ...). Frequently, a busy discovery and/or purification lab receives many such requests and reviewing the results of screens manually is time-consuming and tedious. A tool which automates the task, gathering the required data, scoring the methods for suitability, and reporting the results, is therefore highly desirable.&nbsp;Mgears Chrom Best Method has been developed as a tool to hierarchically organize the results of screening experiments according to flexible, convenient, automated, and user-selectable scoring criteria. In this short note, we will consider, as a case study, the selection of LC procedures to isolate ezetimibe (C24 H21 F2NO3; CAS: 163222-33-1, MW: 409.433 g/mol) from several of its degradants.Degradation processes of ezetimibe have been extensively described in the literature1,2 . The sample mixture was prepared departing from an ezetimibe standard (Acesys Pharmatech, Cat No. 1242-111-21, Fairfield, NJ, USA). This standard was submitted to alkaline and acidic degradation processes, then mixed and spiked with the pure standard to provide a mixture where both the API and several of its degradants can be accurately detected. This mixture was diluted and used for all screening runs described, which were arranged according to a simple factorial experimental design (Table 1).&nbsp;The column was a Cortecs C8 3 x 50 mm, 2.7 um (Waters® PN.186008369). The instrumental system used was a Waters® Acquity HClass LC system furnished with an Acquity DAD-UV detector and a Xevo TQD detector and was operated via the Masslynx software. MSD scan mode signal in the 100-700 m/z range was registered in negative polarity. The DAD wavelength range was 220-400 nm. The mobile phase flowrate was 0.2 mL/min in all experiments. The pH of the mobile phases was varied between 3 and 8, developing elutions with acetonitrile as modifier, at column temperatures of between 35 and 55 ºC.<h4>Chrom Best Method Scoring Criteria</h4>Chrom Best Method provides a highly flexible scoring system that can be adapted to the needs of any chromatographic separation aimed at selecting the optimal purification method. Factors such as the target resolution, purity, symmetry, and retention time can be evaluated by means of desirability functions and combined into an overall measure, allowing the selection of the best separation among those provided in the experiment.Desirability functions for scoring criteria use logistic functions (Figure 1) that represent situations that range from highly undesirable (score = 0) to highly desirable (score = 1). These functions are defined by assigning a value for the 50% score point and a slope (k). Moreover, a weight coefficient can be assigned to reflect the relative importance of each scoring criterion to the user.&nbsp;<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1_Desirability_functions_used_by_Chrom_Best_Method_79cf7ee3cc.jpg" alt="Mestrelab Research: Figure 1. Desirability functions used by Chrom Best Method" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1_Desirability_functions_used_by_Chrom_Best_Method_79cf7ee3cc.jpg 245w," sizes="100vw" width="1179" height="382">Some of these functions are quite straightforward, such as those for peak purity, symmetry, and retention time. For example, the purity criterion is defined by fixing the purity level for P50 (e.g., 95%) and the slope. This allows for more or less demanding conditions for separation. For example, in Figure 2 we see how the purity criterion can be relaxed, changing the slope value even though the P50 condition remains the same. Similar behavior may be found in other unilateral desirability criteria (e.g., resolution and similarity), and even in the retention time criterion, although in this case the desirability function defined is a bilateral one.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_2_Controlling_the_target_peak_purity_criterion_bded6cbe39.jpg" alt="Mestrelab Research - Figure 2. Controlling the target peak purity criterion.jpg" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_2_Controlling_the_target_peak_purity_criterion_bded6cbe39.jpg 245w," sizes="100vw" width="1305" height="510">Resolution criterion is a bit more complex because, to allow maximal flexibility, critical values are defined for peaks eluting before and/or after the target peak. Moreover, the basic Rs criterion is combined with another term that considers the maximum percentage of impurity allowable. In this way, the criterion can be controlled and adapted to virtually any practical situation....

Scoring of LC separation procedures for ezetimibe and its degradants using Mgears Chrom Best Method

<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

<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/article/5170">Get ready for Mbook 4.0!</a> This release introduces a modern, redesigned interface with intuitive navigation and an advanced search filtering system, making ELN management more efficient and user-friendly than ever. With a refined role architecture, enhanced experiment setup, and upgraded inventory management, Mbook 4.0 is built to optimize your laboratory workflow.With Mbook 4.0, effortlessly track projects, manage inventory, generate reports, and collaborate seamlessly - all designed to enhance efficiency and optimize every stage of your workflow. Discover the latest features and improvements below and take your chemistry lab management to the next level.<h4>1. Modern Interface, Multi-Session, and Cross-Device Compatibility</h4>Mbook 4.0 presents a refreshed interface with an intuitive design that simplifies navigation and enhances usability. The improved system integrates advanced filters, enabling you to efficiently search for projects, experiments, groups, users, compounds, or bottles in just a few clicks.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1a_Redesigned_Mbook_interface_3d3f9b7156.webp" alt="Mestrelab Research: Figure 1a. Redesigned Mbook interface" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1a_Redesigned_Mbook_interface_3d3f9b7156.webp 245w," sizes="100vw" width="1920" height="1080">A major productivity boost comes with multi-session support, allowing users to open and manage multiple sessions simultaneously in separate browser tabs or windows. Effortlessly switch between projects without losing progress, compare data across different experiments, or explore your inventory while reviewing a reaction-all without losing progress.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1b_Multitasking_with_multiple_Mbook_sessions_8e72fa13db.webp" alt="Mestrelab Research: Figure 1b. Multitasking with multiple Mbook sessions" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1b_Multitasking_with_multiple_Mbook_sessions_8e72fa13db.webp 245w," sizes="100vw" width="1920" height="1080">Additionally, Mbook is now fully compatible with web browsers on tablets and phones, providing cross-device accessibility, ensuring you can access your ELN anytime, anywhere.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_1c_Mbook_accessible_across_different_devices_a5b4acfe7b.webp" alt="Mestrelab Research: Figure 1c. Mbook accessible across different devices." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_1c_Mbook_accessible_across_different_devices_a5b4acfe7b.webp 245w," sizes="100vw" width="1920" height="1080"><h4>2. Advanced Roles and Customizable Permissions</h4>Mbook 4.0 introduces an enhanced additive role and permissions system, enabling organizations to tailor user access according to specific needs. Assign different roles to users and customize specific permissions for each role or combination of roles, ensuring improved security, seamless collaboration, and optimized workflow efficiency.To further enhance security, new users are required to verify their email before gaining access, ensuring that only legitimate accounts are activated.The new “Inventory manager” role has been introduced to streamline stock management, making it ideal for users dedicated to handling inventory tasks. This role can also be assigned alongside other responsibilities, offering flexibility in lab operations and ensuring more efficient resource management.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_2_New_roles_and_permissions_workflow_including_the_Inventory_manager_role_a7bdbc47ae.webp" alt="Mestrelab Research: Figure 2. New roles and permissions workflow, including the Inventory manager role" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_2_New_roles_and_permissions_workflow_including_the_Inventory_manager_role_a7bdbc47ae.webp 245w," sizes="100vw" width="1920" height="1080"><h4>3. Inventory Improvements</h4>With the new filter capabilities, locating any compound in the Compound DB or in the Stockroom is faster and more efficient, streamlining your Inventory inquiries.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_3a_Advanced_filters_for_quick_compound_search_in_Mbook_Inventory_98a1763802.webp" alt="Mestrelab Research: Figure 3a. Advanced filters for quick compound search in Mbook Inventory" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_3a_Advanced_filters_for_quick_compound_search_in_Mbook_Inventory_98a1763802.webp 245w," sizes="100vw" width="1920" height="1080">Another key feature is the Bottles stock report, now available as a CSV file. This report provides a detailed overview of all bottle-related data in Mbook, allowing for more efficient inventory tracking and seamless in-situ stock verification.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_3b_New_Bottles_Export_report_in_CSV_format_5cea350130.webp" alt="Mestrelab Research: Figure 3b. New Bottles Export report in CSV format" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_3b_New_Bottles_Export_report_in_CSV_format_5cea350130.webp 245w," sizes="100vw" width="1920" height="1080"><h4>4. New Experiments capabilities</h4><h5>Improved reaction scheme</h5>Easily clone compounds with a single step, eliminating the need to redraw molecules and saving valuable time.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_4a_9edb852456.webp" alt="Mestrelab Research: Figure 4a. Clone compounds within a reaction scheme" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_4a_9edb852456.webp 245w," sizes="100vw" width="1920" height="1080"><h5>Enhanced text editor</h5>The experiment description section now includes:<ul><li>New formatting options: customizable text sizes, headers, background text colors.</li><li>The ability to insert images, links, formulas, tables, and quotes.</li><li>Image upload support directly from your PC, tablet, or phone for quick documentation.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_4b_Improved_text_editor_with_enhanced_features_d895ce7a10.webp" alt="Mestrelab Research: Figure 4b. Improved text editor with enhanced features" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_4b_Improved_text_editor_with_enhanced_features_d895ce7a10.webp 245w," sizes="100vw" width="1920" height="1080"><h4>5. Enhanced Printable Reports</h4>All reports in Mbook have been redesigned for a more professional look, ensuring they align with your organization’s branding and compliance needs.<ul><li>Customization Options: Display your organization’s logo or add compliance signing fields.</li><li>Improved Performance: A new report engine significantly reduces generation time, allowing for faster access to reports.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_5_Updated_Mbook_report_templates_6dfdb264e4.webp" alt="Mestrelab Research: Figure 5. Updated Mbook report templates" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_5_Updated_Mbook_report_templates_6dfdb264e4.webp 245w," sizes="100vw" width="1920" height="1080"><h4>6. Compound Exportation</h4>Mbook 4.0 now supports exporting compounds from the Compound Database as CSV or SDF files. Whether you need to export a single compound or a fully configurable list, this feature provides flexibility tailored to your workflow.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_6_Export_Compound_DB_as_CSV_or_SDF_files_88cde33463.webp" alt="Mestrelab Research: Figure 6. Export Compound DB as CSV or SDF files" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_6_Export_Compound_DB_as_CSV_or_SDF_files_88cde33463.webp 245w," sizes="100vw" width="1920" height="1080"><h4>7. Mnova Console Upgrade</h4>The Mnova console in Mbook 4.0 has been updated to version 15.1, bringing enhanced data processing, visualization, and reporting capabilities.<ul><li>Background Processing: The Mnova console now runs in the background, allowing you to continue working in Mbook while receiving a notification once processing is complete.</li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Figure_7_Continue_working_in_Mbook_while_Mnova_15_1_processes_data_in_the_background_24c5a3aafd.webp" alt="Mestrelab Research: Figure 7. Continue working in Mbook while Mnova 15.1 processes data in the background" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Figure_7_Continue_working_in_Mbook_while_Mnova_15_1_processes_data_in_the_background_24c5a3aafd.webp 245w," sizes="100vw" width="1920" height="1080">

Mbook 4.0 What’s New – Top Features

The latest version 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</a> is now available, delivering a powerful set of tools, performance enhancements, and automation capabilities to support and accelerate your analytical workflows.Building on these advancements, Mnova 16 introduces expanded functionalities for NMR, LC/GC-MS, and electronic or vibrational spectroscopy, offering improved ways to process, analyze, and report your analytical data – ensuring greater accuracy, efficiency, and ease of use.<h4>What’s new in Mnova 16?</h4><h5>Discover the Top Features:</h5><ul><li>Enhanced Python Engine – Streamline scripting and automation directly within Mnova.</li><li>Allotrope ASM support – Seamless import/export of NMR data to increase interoperability.</li><li>New Command Palette – Access key functions faster with an intuitive, searchable command bar.</li><li>And more – Explore the full list of <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/resources-by-product/resources-mnova/top-features-in-mnova-16.html?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=june&amp;utm_term=mnova%2016&amp;utm_content=blogpost">Top Features.</a></li></ul>Mnova 16 also introduces significant updates across plugins and bricks like Mnova NMR, Mgears, Mnova Hub, Chrom Cal, qNMR, and more.<blockquote>Check the full details in the <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/changelog/whats-new-in-mnova-16.html?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=june&amp;utm_term=mnova%2016&amp;utm_content=blogpost">Changelog</a>.</blockquote><h5>Ready to upgrade?</h5><ul><li>Don’t miss out on the latest innovations – visit our website to download <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/download?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=june&amp;utm_term=mnova%2016&amp;utm_content=blogpost">Mnova 16</a> and take your workflows to the next level.</li></ul><h5>Not a user yet?&nbsp;</h5><ul><li>Try <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/evaluate/single?utm_source=adv&amp;utm_medium=labrulez&amp;utm_campaign=june&amp;utm_term=mnova%2016&amp;utm_content=blogpost">Mnova 16 free for 45 days</a> and experience the full potential of our latest release.</li></ul>

Mnova 16 is Live!

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

In today’s digital age, the sheer number of solutions available at our fingertips can be overwhelming. A simple search can yield countless results, making it difficult to determine which option truly meets our needs. If you’re a synthetic, organic, medicinal, process, or analytical chemist – or a lab manager or IT/data scientist – working in the pharmaceutical, biotech, or chemical industries, we hope this article will clarify the value and relevance of <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1085">Chrom Reaction Optimization</a> (Chrom RO) for your specific requirements.<h4>Chrom RO Empowering Development (Scale-up) Stages</h4>Do you work in reaction scale up? Are you optimizing conditions, comparing dozens of reaction variables? If so, Chrom RO is built for you.Imagine turning weeks of data crunching into just a few hours or days. Chrom RO is designed to be a game-changer in the development stages of the value chain. By tracking and quantifying a defined set of compounds in a reaction run in parallel under different conditions to employ. This not only accelerates the development process but also ensures that the most effective and efficient methods are identified and utilized for scale-up.<h4>Chrom RO Value in Discovery</h4>During the discovery phases, scientists may encounter reaction optimization while exploring pathways, understanding mechanisms, or conducting impurity profiling. Chrom RO is valuable in these processes, provided they involve studying the same reaction run in parallel under different conditions. When dealing with different reactions, such as parallel synthesis or library screening, customization through scripting is required. If you need assistance with this, please <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/contact">contact our team of experts</a>.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Sci_Y_Figure_1_Chrom_RO_is_ideal_for_scale_up_and_early_stage_investigations_210f1637b0.jpg" alt="SciY: Figure 1. Chrom RO is ideal for scale-up and early-stage investigations." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Sci_Y_Figure_1_Chrom_RO_is_ideal_for_scale_up_and_early_stage_investigations_210f1637b0.jpg 234w," sizes="100vw" width="1800" height="1200"><h4>Handling Large Volumes of Data</h4>One of the standout features of Chrom RO is its ability to handle large chromatographic datasets seamlessly. So, if you regularly perform reaction optimizations and manage high volumes of analytical data, this solution will definitely change the way you tackle your work.Gone are days of tedious manual processing for each sample. Chrom RO automates routine data processing, eliminating human errors such as transcription mistakes and number typos. This leads to better data quality and allows chemists to focus on what they do best – innovating and making informed decisions.<h4>A Must-Have for Automated Reaction Optimization Settings</h4>The integration of lab robotics into chemical synthesis has enabled rapid, closed-loop optimization with minimal human intervention. For users with automated reaction optimization, Chrom RO is essential. It automates data processing and analysis, providing results in dashboards for quick decision-making. This makes Chrom RO a powerful tool in the chemist’s arsenal.<h4>Is Chrom RO Compatible with My Data Formats as Needs?</h4>Chrom RO is compatible with any data format supported by Mnova. You can find a full list of these <a target="_blank" rel="noopener noreferrer" href="https://mestrelab.com/articles/ms-supported-formats.html">supported formats here</a>. Below, we highlight the key features and capabilities of Chrom RO to help you assess how it aligns with your specific requirements:<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Sci_Y_Figure_2_A_complete_and_flexible_framework_for_reaction_monitoring_analysis_and_data_management_9180bce0a5.jpg" alt="SciY: Figure 2. A complete and flexible framework for reaction monitoring, analysis, and data management." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Sci_Y_Figure_2_A_complete_and_flexible_framework_for_reaction_monitoring_analysis_and_data_management_9180bce0a5.jpg 234w," sizes="100vw" width="1800" height="1200">And finally, it is critical to know that Chrom RO is so flexible that it allows you to script what to do with the output, enabling seamless integration with other software you may be using in your lab. This ensures that you can leverage the full potential of Chrom RO without disrupting your existing workflows. Output can be saved to your local folders, SQL and NoSQL databases, or even Electronic Lab Notebooks (ELNs) or Laboratory Information Management Systems (LIMS). These outputs can then be utilized by Business Intelligence (BI) and Artificial Intelligence (AI) tools, further enhancing your data analysis and decision-making processes.

Is Chrom Reaction Optimization for me?

<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

From simple automation to advanced scripting, the integrated Python interpreter gives you the freedom to shape Mnova around your workflows. To help you get started, follow our video series Mnova Scripting with Python: The Holy Grail of Automation? and discover how tailored tools can streamline your analytical work with just a few lines of code. Throughout the series, you will learn how to run Python scripts in Mnova, explore the documentation, install additional Python packages, work with Mnova objects, process and analyze NMR spectra, debug your scripts, and much more. The series concludes with an AI tool designed to classify spectra based on aromatic substitution type.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Sci_Y_Mnova_Scripting_with_Python_The_Holy_Grail_of_Automation_1f1c48e690.jpg" alt="SciY: Mnova Scripting with Python - The Holy Grail of Automation?" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Sci_Y_Mnova_Scripting_with_Python_The_Holy_Grail_of_Automation_1f1c48e690.jpg 245w," sizes="100vw" width="1600" height="900"><blockquote><a target="_blank" rel="noopener noreferrer" href="https://go.sciy.com/l/271522/2025-09-03/43wf86">Download the scripts from the videos and try it yourself.</a></blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=deVdAfbX3jY">Ep. 1: Introduction</a></h4><ul><li>Discover the Mnova Python Engine! Learn what it is, where to find it, and how it helps you automate NMR data analysis and streamline workflows with Python scripting.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=QYyWBCRihOQ">Ep. 2: Why Python? &nbsp;</a></h4><ul><li>Explore why this language is ideal for scientific automation, data processing, and workflow customization inside Mnova.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=eckPwABNIPY">Ep. 3: Hello, Pulpo!</a></h4><ul><li>Run your first Mnova Python script: Hello, Pulpo! Get started with scripting in Mnova and see how simple automation can be.&nbsp;</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=Jwps6cEvqbw">Ep. 4: Using The Python Standard Library</a></h4><ul><li>See how to use Python’s built-in Standard Library to interact with your file system directly from Mnova.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=EtSNGTz3fMM">Ep. 5: PyPI - Install &amp; Use Python Packages</a></h4><ul><li>Discover third-party packages from PyPI. Learn how to install and use NumPy to extend your scripting power.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=gv66Rh_QfUY">Ep. 6: Mnova Extensions to Python &nbsp;</a></h4><ul><li>Install Mnova’s Python extensions! Import .mnova files, access documentation, and expand your scripting capabilities.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=Xhu34ZAY7os">Ep. 7: Documents, Pages, and Items</a></h4><ul><li>Learn to interact with documents, pages, and items to automate analysis, extract data, or create custom workflows.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=25qIbFCeRj4">Ep. 8: NMR Items</a></h4><ul><li>Work with complex NMR spectra using the NMR module. Access and display detailed spectrum information via scripting.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=mNEOowGfa2c">Ep. 9: Applying Processing Templates to NMR Spectra</a></h4><ul><li>Automate your NMR workflows by applying processing templates to spectra directly through Python scripts.&nbsp;</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=xx-nI3H_PqY">Ep. 10: NMR Peaks &amp; Multiplets</a></h4><ul><li>Extract NMR peaks and multiplets from processed spectra to gain detailed analytical insights with ease.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=RvECqA4UpQM">Ep. 11: Mnova’s User Interface</a></h4><ul><li>Make scripts interactive! Use Mnova widgets to create dialogs and user-friendly Python interfaces.</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/watch?v=o1-SZ_po2sc">Ep. 12: Debugging Your Scripts</a></h4><ul><li>Debug smarter with Visual Studio Code. Learn to identify and fix issues quickly for a smoother development process.&nbsp;</li></ul><h4><a target="_blank" rel="noopener noreferrer" href="https://go.sciy.com/l/271522/2025-10-17/4jwld8">Ep. 13: Building an AI Classifier to Detect Aromatic Substitution</a></h4><ul><li>This final episode showcases how seamlessly Mnova integrates with Python to power advanced data analysis and machine learning, bridging the gap between NMR spectroscopy and actionable AI.</li></ul>

Mnova Scripting with Python: The Holy Grail of Automation?

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