Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.
iScience
; 24(9): 103052, 2021 Sep 24.
Article
em En
| MEDLINE
| ID: mdl-34553136
ABSTRACT
Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.
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1
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article