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Dr. Grégory Genta-Jouve

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USR mixte LEEISA (CNRS, Université de Guyane, Ifremer), C-TAC, UMR CNRS 8038 CiTCoM (CNRS, Université Paris Descartes), France
Interests: natural products anticipation; chemical ecology; spectra prediction; metabolomics

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Dear Colleagues,

The identification of natural products has experienced a dramatic increase in the last few years, thanks to important advances in numerical methods. New chemical entities have been reported using computer-assisted methods, in the first steps of isolation and purification, for the identification of the planar structure or for the determination of their relative and absolute configurations.

Recent publications regarding the targeted isolation of new compounds are paving the way for a paradigm shift in natural product chemistry, which is guiding researchers towards previously undescribed computer-generated natural products.

This Special Issue of Molecules is dedicated to original research, review, and opinion articles that deal with numerical methods, their application, or the development of new methods.

Assoc. Prof. Grégory Genta-Jouve
Guest Editor

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Keywords

computer-assisted structure elucidation
DFT
natural product anticipation
extract dereplication
metabolite identification
MetWork
CANPA

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Research

17 pages, 3897 KiB

Open AccessArticle

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils

by Manuela Sabatino, Marco Fabiani, Mijat Božović, Stefania Garzoli, Lorenzo Antonini, Maria Elena Marcocci, Anna Teresa Palamara, Giovanna De Chiara and Rino Ragno

Molecules 2020, 25(10), 2452; https://doi.org/10.3390/molecules25102452 - 25 May 2020

Cited by 18 | Viewed by 3695

Abstract

In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among the prominent studies essential oils are investigated as antibacterial agents alone or in combination with known drugs. Minor studies involved essential oil inspection as potential anticancer and antiviral natural remedies. In line with the authors previous reports the investigation of an in-house library of extracted essential oils as a potential blocker of HSV-1 infection is reported herein. A subset of essential oils was experimentally tested in an in vitro model of HSV-1 infection and the determined IC₅₀s and CC₅₀s values were used in conjunction with the results obtained by gas-chromatography/mass spectrometry chemical analysis to derive machine learning based classification models trained with the partial least square discriminant analysis algorithm. The internally validated models were thus applied on untested essential oils to assess their effective predictive ability in selecting both active and low toxic samples. Five essential oils were selected among a list of 52 and readily assayed for IC₅₀ and CC₅₀ determination. Interestingly, four out of the five selected samples, compared with the potencies of the training set, returned to be highly active and endowed with low toxicity. In particular, sample CJM1 from Calaminta nepeta was the most potent tested essential oil with the highest selectivity index (IC₅₀ = 0.063 mg/mL, SI > 47.5). In conclusion, it was herein demonstrated how multidisciplinary applications involving machine learning could represent a valuable tool in predicting the bioactivity of complex mixtures and in the near future to enable the design of blended essential oil possibly endowed with higher potency and lower toxicity. Full article

(This article belongs to the Special Issue Chemoinformatics of Natural Products Chemistry)

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