Biased Complement Diversity Selection for Effective Exploration of Chemical Space in Hit-Finding Campaigns.
J Chem Inf Model
; 59(5): 1709-1714, 2019 05 28.
Article
em En
| MEDLINE
| ID: mdl-30943027
ABSTRACT
The success of hit-finding campaigns relies on many factors, including the quality and diversity of the set of compounds that is selected for screening. This paper presents a generalized workflow that guides compound selections from large compound archives with opportunities to bias the selections with available knowledge in order to improve hit quality while still effectively sampling the accessible chemical space. An optional flag in the workflow supports an explicit complement design function where diversity selections complement a given core set of compounds. Results from three project applications as well as a literature case study exemplify the effectiveness of the approach, which is available as a KNIME workflow named Biased Complement Diversity (BCD).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
3_ND
Problema de saúde:
3_malaria
/
3_neglected_diseases
Assunto principal:
Descoberta de Drogas
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos