Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/61667
Título: | Artificial intelligence in biological activity prediction |
Autor(es): | Correia, João Resende, Tiago F. Baptista, Delora Rocha, Miguel |
Palavras-chave: | Machine learning Deep learning Biological activity prediction Sweetness prediction Compound featurization |
Data: | 2020 |
Editora: | Springer |
Revista: | Advances in Intelligent Systems and Computing |
Citação: | Correia, João; Resende, Tiago F.; Baptista, Delora; Rocha, Miguel, Artificial intelligence in biological activity prediction. Advances in Intelligent Systems and Computing. Vol. 1005 (PACBB 2019), Springer, 164-172, 2020. |
Resumo(s): | Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/61667 |
ISBN: | 9783030238728 |
DOI: | 10.1007/978-3-030-23873-5_20 |
ISSN: | 2194-5357 |
e-ISSN: | 2194-5365 |
Versão da editora: | http://www.springer.com/series/11156 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_51778_1.pdf | 208,06 kB | Adobe PDF | Ver/Abrir |