title: Machine-learning techniques applied to antibacterial drug discovery.
abstract: The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.
authors.affiliation: Department of Chemistry & Biochemistry and the National Biomedical Computation Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
authors.forename: Jacob D
authors.initials: JD
authors.lastname: Durrant
authors.forename: Rommie E
authors.initials: RE
authors.lastname: Amaro
mesh_terms: D000900:Anti-Bacterial Agents; D017076:Computer-Aided Design; D055808:Drug Discovery / Q000379:methods*; D016571:Neural Networks, Computer
chemical_list: D000900:Anti-Bacterial Agents
keywords: antibiotics; drug discovery; machine learning; molecular recognition; structure-based drug design; virtual screening