Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.
Int J Mol Sci
; 22(14)2021 Jul 19.
Article
em En
| MEDLINE
| ID: mdl-34299333
ABSTRACT
In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.
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Base de dados:
MEDLINE
Assunto principal:
Avaliação Pré-Clínica de Medicamentos
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Máquina de Vetores de Suporte
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Inibidores de Protease de Coronavírus
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SARS-CoV-2
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Tratamento Farmacológico da COVID-19
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article