Exploiting machine learning for end-to-end drug discovery and development.
Nat Mater
; 18(5): 435-441, 2019 05.
Article
em En
| MEDLINE
| ID: mdl-31000803
ABSTRACT
A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Biologia Computacional
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article