This file contains the data to reproduce the models proposed in the following manuscript: Multi-task neural networks and molecular fingerprints to enhance compound identification from LC-MS/MS data, submitted to Molecules (2022) [link]. In this study, deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra have been developed.
Conditions and warranty
The data are freeware and may be used if proper reference is given to the authors. Please, refer to the following paper.
V. Consonni, F. Gosetti, V. Termopoli, R. Todeschini, C. Valsecchi, D. Ballabio (2022) Multi-task neural networks and molecular fingerprints to enhance compound identification from LC-MS/MS data, Molecules, 27, 5827 [link]
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Fill in the following form. Your personal data will be used only for notification via email of new releases of the dataset and will not be communicated to external third parties. Once the form has been submitted, open the rar file and extract the files. Have a look to the readme.txt file for further details. If you experience any problem to downlaod the toolbox, write to davide.ballabio@unimib.it.