Antibiotic Identified by AI

Computational approaches are emerging as powerful tools for the discovery of antibiotics. A study now uses machine learning to discover abaucin, a potent antibiotic that targets the bacterial pathogen Acinetobacter baumannii.

Traditionally, antibiotics were discovered by the screening of soil microorganisms, seeking secondary metabolites. From these initial molecules, numerous derivatives were synthesized, showing improved bioactivities and greater potential to meet clinical standards. Until recently, discovery efforts relied on the painstaking screening of large compound libraries for antibiotic activity1. Phenotypic screening approaches do not require advanced knowledge of the mechanism of action; however, the extensive and unexplored chemical space in the search for drug-like molecules poses considerable challenges, as the screening of numerous potential combinations (of both compounds and different bacterial strains) is both expensive and time-consuming. Thus, the discovery process of a new antibiotic has consistently depended on chance and serendipity. Recent advances that incorporate cutting-edge artificial intelligence (AI) and other computational approaches have demonstrated remarkable success in accelerating the identification of new drugs with desired properties2,3,4,5,6,7,8,9,10. Reporting in Nature Chemical Biology, Liu et al.11 have applied a machine learning algorithm to rapidly predict the antimicrobial activity of molecules against the Gram-negative pathogen Acinetobacter baumannii — which is a cause of nosocomial (hospital-derived) infections.

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Fig. 1: The discovery of abaucin driven by machine learning.

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Author information

Authors and Affiliations

Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Angela Cesaro & Cesar de la Fuente-Nunez

Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

Angela Cesaro & Cesar de la Fuente-Nunez

Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA

Angela Cesaro & Cesar de la Fuente-Nunez

Corresponding author

Correspondence to
Cesar de la Fuente-Nunez.

Ethics declarations

Competing interests

C.F.-N. provides consulting services to Invaio Sciences and is a member of the scientific advisory boards of Nowture S.L. and Phare Bio. The de la Fuentel lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble, none of which were used in support of this work.