Comprehensive ensemble in QSAR prediction for drug discovery

Background Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based mach...

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Veröffentlicht in:BMC bioinformatics Jg. 20; H. 1; S. 521 - 12
Hauptverfasser: Kwon, Sunyoung, Bae, Ho, Jo, Jeonghee, Yoon, Sungroh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 26.10.2019
BMC
Schlagworte:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Drug Discovery - methods
Drug-prediction
Ensemble-learning
Life Sciences
Machine Learning
Machine Learning and Artificial Intelligence in Bioinformatics
Meta-learning
Methodology
Methodology Article
Microarrays
Quantitative Structure-Activity Relationship
Ensemble-learning
Drug-prediction
Meta-learning
ISSN:1471-2105, 1471-2105
Online-Zugang:Volltext
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Zusammenfassung:Background Quantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject. Results The proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/ . Conclusions We propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3135-4