Mnova

Mnova is a professional analytical chemistry software suite for processing, analyzing, visualizing, and reporting data from NMR, LC-MS/GC-MS, and spectroscopy within a single, vendor-neutral platform. Its modular design lets labs tailor workflows from routine QC to advanced research, while maintaining consistency across datasets. 

Trusted by over 8,000 organizations worldwide, including 41 of the top 50 pharmaceutical companies, Mnova supports groundbreaking discoveries and unifies disparate data under one platform. In academia, it leads NMR data processing, having analyzed millions of spectra to accelerate research and enhance outcomes globally. By reducing software fragmentation and minimizing tool switching, Mnova enables labs to work faster, smarter, and more efficiently, delivering reliable results across the entire analytical workflow.

Pre-Packaged Mnova Combos

Mnova’s pre-packaged combos bundle the most-used modules into cost-efficient solutions that deliver more capability for less money. You get the tools labs actually need making it the smartest way to maximize value per license.

1. Ndukwe, I.E., Elipe, M.V.S., Quasdorf, K., Navarro-Vázquez, A., Mardirossian, N., Khachatrian, D., Roosen, P.C. and Smith, A., Anisotropic Nuclear Magnetic Resonance Spectroscopy and Density Functional Theory Methodologies Combine with CASE-3D Analysis for Unambiguous Diastereomeric Differentiation of AMG 176 Macrocycles. ChemistryEurope, 202500074 (2025). https://doi.org/10.1002/ceur.202500074 
2. Cui-Mei Liu,  Wei Jia, Zhen-Dong Hua Automated rapid screening of new psychoactive substances using benchtop NMR technology combined with database search, Forensic Science International 375, (2025). https://doi.org/10.1016/j.forsciint.2025.112564
3. D. Williamson, S. Ponte, I. Iglesias, N. Tonge, C. Cobas, E.K. Kemsley Chemical shift prediction in 13C NMR spectroscopy using ensembles of message passing neural networks (MPNNs) Journal of Magnetic Resonance, Volume 368, (2024). https://doi.org/10.1016/j.jmr.2024.107795 
4. Cobas, C., García-Pulido, J. A., Mora, P., Selva, G., Sykora, S. A New qNMR Compliant Savitzky-Golay Apodization Function for Resolution Enhancement. Magnetic Resonance in Chemistry 63, 90-97 (2025). https://doi.org/10.1002/mrc.5492
5. Ossorio-Castillo, J., Rodríguez-Coello, A., Simulating NMR Spectra with a Quantum Computer. arXiv preprint arXiv:2410.20836, (2024). https://arxiv.org/abs/2410.20836
6. Costa, P.M., Lysak, D.H., Soong, R., Ronda, K., Wolff, W. W., Downey, K., Steiner, K., Moxley-Paquette, V., Pellizzari, J., Anklin, C., Sharman, G., Cobas, C., Domínguez, S., Jobst, K. J., Cahill, L., Simpson, M. J., & Simpson, A. J. Development of a Simple Cost Effective Oxygenation System for In Vivo Solution State NMR in 10 mm NMR Tubes. Analytical Chemistry 96, 12667-12675 (2024). https://doi.org/10.1021/acs.analchem.4c01390 
7. Pérez Varela, I., Shear, G., Cobas, C. Molecular Melodies: Unraveling the Hidden Harmonies of NMR Spectroscopy. Molecules 29, 762 (2024). https://doi.org/10.3390/molecules29040762 
8. Góñez, K. V., García, J. S., Sardina, F. J., Pazos, Y., Saá, Á., Martín−Pastor, M. J-filter: An experiment to simplify and isolate specific signals in 1H NMR spectra of complex mixtures based on scalar coupling constants. Magnetic Resonance in Chemistry 61, 615 (2023). https://doi.org/10.1002/mrc.5396
9. Kuhn, S., Cobas, C., Barba, A., Colreavy-Donnelly, S., Caraffini, F., Borges, R. M. Direct deduction of chemical class from NMR spectra. Journal of Magnetic Resonance 348, 107381(2023). https://doi.org/10.1016/j.jmr.2023.107381
10. Peng, C., Namanja, A. T., Munoz, E., Wu, H., Frederick, T. E., Maestre-Martinez, M., Iglesias Fernandez, I., Sun, Q., Cobas, C., Sun, C., Petros, A. M. Efficiently driving protein-based fragment screening and lead discovery using two-dimensional NMR. Journal of Biomolecular NMR 77, 39-53 (2023). https://doi.org/10.1007/s10858-022-00410-3 
11. Soong, R., Downey, K., Moser, A., Monje, P., Jenne, A., Ghosh Biswas, R., Bastawrous, M., Majumdar, R., Lysak, D. H., Adamo, A., Goerling, B., Decker, V., Busse, F., Dominguez, S., Sauer, E., Mikhaylichenko, S., Luk, V., Simpson, A. J. A CASE (Computer-Assisted Structure Elucidation) for Bench-Top NMR Systems in the Undergraduate Laboratory for De Novo Structure Determination: How Well Can We Do?. Journal of Chemical Education 99, 3780-3788 (2022). https://doi.org/10.1021/acs.jchemed.2c00475
12. Lumley, J. A., Sharman, G., Wilkin, T., Hirst, M., Cobas, C., Goebel, M. A KNIME Workflow for Automated Structure Verification. SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, 950-956 (2020). https://doi.org/10.1177/2472555220907091 
13. Cobas, C. NMR signal processing, prediction, and structure verification with machine learning techniques. Magnetic Resonance in Chemistry 58, 512-519 (2020). https://doi.org/10.1002/mrc.4989 
14. Gallo, V., Ragone, R., Musio, B. et al. A Contribution to the Harmonization of Non-targeted NMR Methods for Data-Driven Food Authenticity Assessment. Food Analytical Methods 13, 530–541 (2020). https://doi.org/10.1007/s12161-019-01664-8
15. Barba, A., Dominguez, S., Cobas, C., Martinsen, D. P., Romain, C., Rzepa, H. S., Seoane, F. Workflows Allowing Creation of Journal Article Supporting Information and Findable, Accessible, Interoperable, and Reusable (FAIR)-Enabled Publication of Spectroscopic Data. ACS Omega 4, 3280-3286 (2019). https://doi.org/10.1021/acsomega.8b03005