A combined drug discovery strategy based on machine learning and molecular docking.
Chem Biol Drug Des
; 93(5): 685-699, 2019 05.
Article
em En
| MEDLINE
| ID: mdl-30688405
ABSTRACT
Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross-validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure-based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Simulação de Acoplamento Molecular
/
Aprendizado de Máquina
Tipo de estudo:
Clinical_trials
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article