Diversity and Chemical Library Networks of Large Data Sets.
J Chem Inf Model
; 62(9): 2186-2201, 2022 05 09.
Article
em En
| MEDLINE
| ID: mdl-34723537
ABSTRACT
The quantification of chemical diversity has many applications in drug discovery, organic chemistry, food, and natural product chemistry, to name a few. As the size of the chemical space is expanding rapidly, it is imperative to develop efficient methods to quantify the diversity of large and ultralarge chemical libraries and visualize their mutual relationships in chemical space. Herein, we show an application of our recently introduced extended similarity indices to measure the fingerprint-based diversity of 19 chemical libraries typically used in drug discovery and natural products research with over 18 million compounds. Based on this concept, we introduce the Chemical Library Networks (CLNs) as a general and efficient framework to represent visually the chemical space of large chemical libraries providing a global perspective of the relation between the libraries. For the 19 compound libraries explored in this work, it was found that the (extended) Tanimoto index offers the best description of extended similarity in combination with RDKit fingerprints. CLNs are general and can be explored with any structure representation and similarity coefficient for large chemical libraries.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Produtos Biológicos
/
Bibliotecas de Moléculas Pequenas
Idioma:
En
Revista:
J Chem Inf Model
Assunto da revista:
INFORMATICA MEDICA
/
QUIMICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos