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Welcome to eo.3d-qsar.com

Py-EO is a new database with the aim to collect as much as possible experimental data on natural compounds, maily focused on Essential oils.
In this db new chemical composition and associated biological activity (activities) for a given sample will be stored.
Apart from the collection of data another important goal is the future application of Machine Learning algorithms to build classification and regression models elaborated on the basis of chemical composition as independent variable and a selected biological behaviour (i.e. anti-candida or anti-oxidant activity) as dependent variable to obtain Quantitative Composition-Activity Relationships to be used for the prediction of untested chemical mixtures as Essential Oils or similar.

If you use this webservice, please let us know and cite:
Patsilinakos, A.; Artini, M.; Papa, R.; Sabatino, M.; Božović, M.; Garzoli, S.; Vrenna, G.; Buzzi, R.; Manfredini, S.; Selan, L.; Ragno, R. Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species. Molecules 2019, 24, 890.

Artini, M.; Patsilinakos, A.; Papa, R.; Božović, M.; Sabatino, M.; Garzoli, S.; Vrenna, G.; Tilotta, M.; Pepi, F.; Ragno, R.; Selan, L. Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa. Molecules 2018, 23, 482.

Sabatino, M.; Fabiani, M.; Božović, M.; Garzoli, S.; Antonini, L.; Marcocci, M.E.; Palamara, A.T.; De Chiara, G.; Ragno, R. Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils. Molecules 2020, 25, 2452.