Investigating the prospects of ChatGPT in training medicinal chemists and the development of novel drugs (2024)
- Authors:
- USP affiliated authors: TROSSINI, GUSTAVO HENRIQUE GOULART - FCF ; ALMEIDA, MICHELL DE OLIVEIRA - FCF ; SOARES, ARTUR CAMINERO GOMES - FCF ; SOUSA, GUSTAVO HENRIQUE MARQUES - FCF ; FERRAZ, WÍTOR RIBEIRO - FCF
- Unidade: FCF
- DOI: 10.17807/orbital.v16i4.21129
- Subjects: PLANEJAMENTO DE FÁRMACOS; INTELIGÊNCIA ARTIFICIAL; PROCESSAMENTO DE LINGUAGEM NATURAL; QUÍMICA MÉDICA
- Language: Inglês
- Imprenta:
- Publisher place: Campo Grande
- Date published: 2024
- Source:
- Título: Orbital: The Electronic Journal of Chemistry
- ISSN: 1984-6428
- Volume/Número/Paginação/Ano: v. 16,n. 4, p. 319-324, 2024
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by-nc-nd
-
ABNT
ALMEIDA, Michell de Oliveira et al. Investigating the prospects of ChatGPT in training medicinal chemists and the development of novel drugs. Orbital: The Electronic Journal of Chemistry, v. 16, n. 4, p. 319-324, 2024Tradução . . Disponível em: https://dx.doi.org/10.17807/orbital.v16i4.21129. Acesso em: 28 dez. 2025. -
APA
Almeida, M. de O., Soares, A. C. G., Sousa, G. H. M. de, Ferraz, W. R., & Trossini, G. H. G. (2024). Investigating the prospects of ChatGPT in training medicinal chemists and the development of novel drugs. Orbital: The Electronic Journal of Chemistry, 16( 4), 319-324. doi:10.17807/orbital.v16i4.21129 -
NLM
Almeida M de O, Soares ACG, Sousa GHM de, Ferraz WR, Trossini GHG. Investigating the prospects of ChatGPT in training medicinal chemists and the development of novel drugs [Internet]. Orbital: The Electronic Journal of Chemistry. 2024 ; 16( 4): 319-324.[citado 2025 dez. 28 ] Available from: https://dx.doi.org/10.17807/orbital.v16i4.21129 -
Vancouver
Almeida M de O, Soares ACG, Sousa GHM de, Ferraz WR, Trossini GHG. Investigating the prospects of ChatGPT in training medicinal chemists and the development of novel drugs [Internet]. Orbital: The Electronic Journal of Chemistry. 2024 ; 16( 4): 319-324.[citado 2025 dez. 28 ] Available from: https://dx.doi.org/10.17807/orbital.v16i4.21129 - Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors
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Informações sobre o DOI: 10.17807/orbital.v16i4.21129 (Fonte: oaDOI API)
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