Machine Learning Methods in Drug Discovery.
Molecules
; 25(22)2020 Nov 12.
Article
en En
| MEDLINE
| ID: mdl-33198233
ABSTRACT
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Biología Computacional
/
Descubrimiento de Drogas
/
Aprendizaje Automático
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Molecules
Asunto de la revista:
BIOLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos