A multi-reference poly-conformational method for <i>in silico</i> design, optimization, and repositioning of pharmaceutical compounds illustrated for selected SARS-CoV-2 ligands.

Alexandrov, Vadim; Kirpich, Alexander; Kantidze, Omar; Gankin, Yuriy

A multi-reference poly-conformational method for in silico design, optimization, and repositioning of pharmaceutical compounds illustrated for selected SARS-CoV-2 ligands.

Alexandrov, Vadim; Kirpich, Alexander; Kantidze, Omar; Gankin, Yuriy.

Afiliação

Alexandrov V; Liquid Algo LLC, Hopewell Junction, NY, United States of America.
Kirpich A; Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, United States of America.
Kantidze O; Quantori, Cambridge, MA, United States of America.
Gankin Y; Quantori, Cambridge, MA, United States of America.

PeerJ ; 10: e14252, 2022.

Article em En | MEDLINE | ID: mdl-36447514

Assuntos

COVID-19; SARS-CoV-2; Humanos; Reposicionamento de Medicamentos; Conformação Molecular; Ligantes; Preparações Farmacêuticas

Palavras-chave

Computational framework; Conformers; In silico; Ligand-based; Multi-reference; Poly-conformational; SARSCOV- 2; Structure-based; Validation; Virtual library

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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