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Explore thousand of essential oils based on hundreds of publications

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The AI4EssOil Project

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.


Biological activities

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Predict EO Activity

Select a target in the following list and predict your EO activity against it.

Antitumor effect of Melaleuca alternifolia essential oil and its main component terpinen-4-ol in combination with target therapy in melanoma models Di Martile, M.; Garzoli, S.; Sabatino, M.; Valentini, E.; D’Aguanno, S; Ragno, R.; Del Bufalo, D. Cell Death Discovery Vol 7, 127 (2021) Essential Oils Biofilm Modulation Activity, Chemical and Machine Learning Analysis. Application on Staphylococcus aureus Isolates from Cystic Fibrosis Patients Papa, R.; Garzoli, S.; Vrenna, G.; Sabatino, M.; Sapienza, F.; Relucenti, M.; Donfrancesco, O.; Fiscarelli, E.; Artini, M.; Selan, L; Ragno, R. Int J Mol Sci. 2020 Dec 4;21(23):9258 Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches Ragnp, R.; Papa, R.; Patsilinakos, A; Vrenna, G.; Garzoli, S.; Tuccio, V.; Fiscarelli, E.; Selan, L.; Artini, M.; Int J Mol Sci. 2020 Dec 4;21(23):9258 Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species Patsilinakos, A.; Artini, M.; Papa, R.; Sabatino, M.; Božović, M.; Garzoli, S.; Vrenna, G.; Buzzi, R.; Manfredini, S.; Selan, L.; Ragno, R. Molecules 2019, 24(5), 890 Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa Artini, M.; Patsilinakos, A.; Papa, R.; Božović, M.; Sabatino, M.; Garzoli, S.; Vrenna, G.; Tilotta, M.; Pepi, F.; Ragno, R.; Selan, L. Molecules 2018, 23(2), 482 Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils Sabatino, M.; Fabiani, M.; Božović, M.; Garzoli, S.; Antonini, L.; Marcocci, M.E.; Palamara, A.T.; De Chiara, G.; Ragno, R. Molecules 2020, 25(10), 2452

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