Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.
Brief Bioinform
; 20(4): 1337-1357, 2019 07 19.
Article
em En
| MEDLINE
| ID: mdl-29377981
ABSTRACT
Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Descoberta de Drogas
/
Desenvolvimento de Medicamentos
Tipo de estudo:
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article