The application of machine learning techniques to innovative antibacterial discovery and development

Author(s)

Serafim, Mateus Sa Magalhaes ^[1] ; Kronenberger, Thales ^[2] ; Oliveira, Patricia Rufino ^[3] ; Poso, Antti ^{[2, 4]} ; Honorio, Kathia Maria ^{[3, 5]} ; Mota, Bruno Eduardo Fernandes ^[6] ; Maltarollo, Vinicius Goncalves ^[7]

Affiliation

^[1] Univ Fed Minas Gerais UFMG, Inst Ciencias Biol, Dept Microbiol, Belo Horizonte, MG - Brazil

^[2] Univ Hosp Tubingen, Dept Internal Med 8, Tubingen - Germany

^[3] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, Sao Paulo - Brazil

^[4] Univ Eastern Finland, Sch Pharm, Fac Hlth Sci, Kuopio - Finland

^[5] Univ Fed ABC, Ctr Ciencias Nat & Humanas, Santo Andre, SP - Brazil

^[6] Univ Fed Minas Gerais UFMG, Fac Farm, Dept Anal Clin & Toxicol, Belo Horizonte, MG - Brazil

^[7] Univ Fed Minas Gerais UFMG, Fac Farm, Dept Prod Farmaceut, Belo Horizonte, MG - Brazil

• Document type: Review article
• Source: EXPERT OPINION ON DRUG DISCOVERY; v. 15, n. 10 JUN 2020.
• Web of Science Citations: 0

Abstract

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. (AU)

• FAPESP's process: 16/24524-7 - STRUCTURAL ANALYSIS AND MOLECULAR MODELING STUDIES FOR NATURAL AND SYNTHETIC LIGANTS RELATED TO NEGLECTED DISEASES
• Grantee: Kathia Maria Honorio
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/10118-0 - Study and application of electrochemical technology for the analysis and degradation of endocrine interferents: materials, sensors, processes and scientific dissemination
• Grantee: Marcos Roberto de Vasconcelos Lanza
• Support Opportunities: Research Projects - Thematic Grants