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eNauka >  Results >  Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms
Title: Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms
Authors: Astolfi, Roberta; Oliva, Alessandra; Raffo, Antonio; Sapienza, Filippo; Ragno, Alessio; Proia, Eleonora; Mastroianni, Claudio M; Luceri, Cristina; Bozovic, Mijat; Mladenovic, Milan;
Papa, Rosanna; Bottoni, Patrizia; Mazzinelli, Elena; Nocca, Giuseppina; Ragno, Rino;
Issue Date: 2025
Publication: JOURNAL OF CHEMICAL INFORMATION AND MODELING
ISSN: 1549-9596 Journal of Chemical Information and Modeling Search Idenfier
Type: Article
Collation: vol. 65 br. 3 str. 1378-1391
DOI: 10.1021/acs.jcim.4c02389
WoS-ID: 001403553800001
Scopus-ID: 2-s2.0-85215935824
URI: https://enauka.gov.rs/handle/123456789/966174
Project: ARSIAL
Metadata source: (Preuzeto iz Nasi u WoS)
M-category: 
21aM21a

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