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Daynac, M, Cortes-Cabrera, A and Prieto, JM (2015) Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE, 2015. ISSN 1741-427X
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Abstract
Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.
| Item Type: | Article |
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| Uncontrolled Keywords: | 1104 Complementary and Alternative Medicine |
| Subjects: | Q Science > QD Chemistry R Medicine > RM Therapeutics. Pharmacology R Medicine > RZ Other systems of medicine |
| Divisions: | Pharmacy & Biomolecular Sciences |
| Publisher: | HINDAWI LTD |
| Related URLs: | |
| Date Deposited: | 14 Jul 2020 14:55 |
| Last Modified: | 04 Sep 2021 07:00 |
| DOI or ID number: | 10.1155/2015/561024 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/13303 |
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