ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.
Nucleic Acids Res
; 51(W1): W25-W32, 2023 07 05.
Article
em En
| MEDLINE
| ID: mdl-37158247
ABSTRACT
Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http//chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Avaliação Pré-Clínica de Medicamentos
/
Descoberta de Drogas
/
Visualização de Dados
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Ano de publicação:
2023
Tipo de documento:
Article