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Comprehensive ensemble in QSAR prediction for drug discovery
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- Authors
- Issue Date
- 2019-10-26
- Publisher
- BMC
- Citation
- BMC Bioinformatics, 20(1):521
- Keywords
- Ensemble-learning ; Meta-learning ; Drug-prediction
- Abstract
- 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.
- ISSN
- 1471-2105
- Language
- English
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