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An Acad Bras Cienc ; 96(4): e20230756, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39383429

RESUMO

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we consolidate a database with 15945 AMPs and 12535 non-AMPs taken as the base to train a pool of supervised learning models to recognize peptides with antimicrobial activity. Results show that the proposed tool (AmpClass) outperforms classical state-of-the-art prediction models and achieves similar results compared with deep learning models.


Assuntos
Peptídeos Antimicrobianos , Aprendizado de Máquina Supervisionado , Peptídeos Antimicrobianos/farmacologia , Peptídeos Antimicrobianos/química
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