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Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities.
Toussi, Cyrus Ahmadi; Haddadnia, Javad; Matta, Chérif F.
Afiliação
  • Toussi CA; Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran.
  • Haddadnia J; Department of Chemistry, Dalhousie University, Halifax, NS, B3H4J3, Canada.
  • Matta CF; Department of Chemistry and Physics, Mount Saint Vincent University, Halifax, NS, B3M2J6, Canada.
Mol Divers ; 25(2): 899-909, 2021 May.
Article em En | MEDLINE | ID: mdl-32222890
An elastic network model (ENM) represents a molecule as a matrix of pairwise atomic interactions. Rich in coded information, ENMs are hereby proposed as a novel tool for the prediction of the activity of series of molecules, with widely different chemical structures, but a common biological activity. The new approach is developed and tested using a set of 183 inhibitors of serine/threonine-protein kinase enzyme (Plk3) which is an enzyme implicated in the regulation of cell cycle and tumorigenesis. The elastic network (EN) predictive model is found to exhibit high accuracy and speed compared to descriptor-based machine-trained modeling. EN modeling appears to be a highly promising new tool for the high demands of industrial applications such as drug and material design.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Moleculares / Proteínas Serina-Treonina Quinases / Proteínas Supressoras de Tumor / Inibidores de Proteínas Quinases Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Moleculares / Proteínas Serina-Treonina Quinases / Proteínas Supressoras de Tumor / Inibidores de Proteínas Quinases Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article