With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However,... Show moreWith the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers' time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research. Show less
Henquet, M.G.L.; Roelse, M.; Vos, R.C.H. de; Schipper, A.; Polder, G.; Ruijter, N.C.A. de; ... ; Jongsma, M.A. 2016
Undoubtedly, grapes and wine are globally the most important fruit and food commodities, respectively. The first objective of this research is to optimize an extraction protocol suitable for grape... Show moreUndoubtedly, grapes and wine are globally the most important fruit and food commodities, respectively. The first objective of this research is to optimize an extraction protocol suitable for grape metabolic profiling followed by the application of that protocol to perform metabolic characterization of grape cultivar, wine types, and vintage using NMR spectroscopy in combination with chemometrics methods. This approach was also used to study different physiological processes in grapevine like ripening of berry and resistance against fungal pathogen. Metabolic characterization of different white wines to highlight the metabolites responsible for sensory attributes was also carried out. Another task of this research was to correlate the metabolic profiling data from grapes and wine with the bioactivity data using various multivariate regression models. Show less
