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Comprehensive ensemble in QSAR prediction for drug discovery

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Authors
Kwon, Sunyoung; Bae, Ho; Jo, Jeonghee; Yoon, Sungroh
Issue Date
2019-10-26
Publisher
BMC
Citation
BMC Bioinformatics, 20(1):521
Keywords
Ensemble-learningMeta-learningDrug-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
URI
https://doi.org/10.1186/s12859-019-3135-4

http://hdl.handle.net/10371/164398
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College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Journal Papers (저널논문_전기·정보공학부)
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