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.
