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7126030 
Journal Article 
The application of machine learning techniques to innovative antibacterial discovery and development 
Serafim, MSaM; Kronenberger, T; Oliveira, PR; Poso, A; Honorio, KM; Mota, BEF; Maltarollo, VG; , 
2020 
Yes 
Expert Opinion on Drug Discovery
ISSN: 1746-0441 
TAYLOR & FRANCIS LTD 
ABINGDON 
1165-1179 
32552005 
WOS:000545725900001 
Introduction After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds). Areas covered In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years. Expert opinion The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.