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

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo de Revisão
• Fonte: EXPERT OPINION ON DRUG DISCOVERY; v. 15, n. 10 JUN 2020.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 16/24524-7 - Análise estrutural e estudos de modelagem molecular para ligantes de origem natural e sintética relacionados a doenças negligenciadas
• Beneficiário: Kathia Maria Honorio
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 17/10118-0 - Estudo e aplicação da tecnologia eletroquímica para a análise e a degradação de interferentes endócrinos: materiais, sensores, processos e divulgação científica
• Beneficiário: Marcos Roberto de Vasconcelos Lanza
• Linha de fomento: Auxílio à Pesquisa - Temático