On the virtues of automated quantitative structure-activity relationship: the new kid on the block

Autor(es)

de Oliveira, Marcelo T. ^{[1, 2]} ; Katekawa, Edson ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Quim Sao Carlos, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Fis Sao Carlos, Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Future Medicinal Chemistry; v. 10, n. 3, p. 335-342, FEB 2018.
• Citações Web of Science: 3

Resumo

Quantitative structure-activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued. (AU)

• Processo FAPESP: 13/07600-3 - CIBFar - Centro de Inovação em Biodiversidade e Fármacos
• Beneficiário: Glaucius Oliva
• Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 14/50623-7 - Descoberta e desenvolvimento de ativo cosmético para o controle da oleosidade: estudos in silico e in vitro
• Beneficiário: Edson Katekawa
• Linha de fomento: Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE