Antibiotic discovery with artificial intelligence for the treatment of
Acinetobacter baumannii
infections
Authors: Boulaamane Yassir , Molina Panadero Irene , Hmadcha Abdelkrim , Atalaya Rey Celia , Baammi Soukayna , El Allali Achraf , Maurady Amal , Smani Younes 
Published: May 2024

Global challenges presented by multidrug-resistant
Acinetobacter baumannii
infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in
A. baumannii
. Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against
A. baumannii
followed by a structure-based virtual screening against OmpW.
In silico
pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from −7.8 to −7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of
A. baumannii
strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all
A. baumannii
strains. Finally, the compound was found to significantly reduce the
A. baumannii
interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating
A. baumannii
infections.

IMPORTANCE

Acinetobacter baumannii
presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in
A. baumannii
and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against
A. baumannii
, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing
A. baumannii
interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against
A. baumannii
infections, offering a promising strategy to address antibiotic resistance.