Deep learning and virtual drug screening.
Future Med Chem
; 10(21): 2557-2567, 2018 11.
Article
em En
| MEDLINE
| ID: mdl-30288997
ABSTRACT
Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads more efficiently and accurately. Herein, we explain the broad basics and integration of both virtual screening (VS) and ML. We then discuss artificial neural networks (ANNs) and their usage for VS. The ANN is emerging as the dominant classifier for ML in general, and has proven its utility for both structure-based and ligand-based VS. Techniques such as dropout, multitask learning and convolution improve the performance of ANNs and enable them to take on chemical meaning when learning about the drug-target-binding activity of compounds.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Descoberta de Drogas
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article