Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade (2021)
- Authors:
- Autor USP: HONORIO, KÁTHIA MARIA - EACH
- Unidade: EACH
- DOI: 10.1080/17460441.2021.1918098
- Subjects: ANTIVIRAIS; PLANEJAMENTO DE FÁRMACOS; COMPUTAÇÃO APLICADA
- Language: Inglês
- Imprenta:
- Publisher place: United Kingdom
- Date published: 2021
- Source:
- Título: EXPERT OPINION ON DRUG DISCOVERY
- ISSN: 1746-0441
- Volume/Número/Paginação/Ano: v. 16, n. 9, p. 961-975, 2021
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SERAFIM, Mateus Sá Magalhães et al. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. EXPERT OPINION ON DRUG DISCOVERY, v. 16, n. 9, p. 961-975, 2021Tradução . . Disponível em: https://doi.org/10.1080/17460441.2021.1918098. Acesso em: 03 mar. 2026. -
APA
Serafim, M. S. M., Júnior, V. S. dos S., Gertrudes, J. C., Maltarollo, V. G., & Honorio, K. M. (2021). Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. EXPERT OPINION ON DRUG DISCOVERY, 16( 9), 961-975. doi:10.1080/17460441.2021.1918098 -
NLM
Serafim MSM, Júnior VS dos S, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade [Internet]. EXPERT OPINION ON DRUG DISCOVERY. 2021 ; 16( 9): 961-975.[citado 2026 mar. 03 ] Available from: https://doi.org/10.1080/17460441.2021.1918098 -
Vancouver
Serafim MSM, Júnior VS dos S, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade [Internet]. EXPERT OPINION ON DRUG DISCOVERY. 2021 ; 16( 9): 961-975.[citado 2026 mar. 03 ] Available from: https://doi.org/10.1080/17460441.2021.1918098 - Virtual screening and in vitro assays of novel hits as promising DPP-4 inhibitors
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Informações sobre o DOI: 10.1080/17460441.2021.1918098 (Fonte: oaDOI API)
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