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
de 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).
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Découverte de médicament
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
Limites:
Animals
/
Humans
Langue:
En
Journal:
J Chem Inf Model
Sujet du journal:
INFORMATICA MEDICA
/
QUIMICA
Année:
2019
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique