Comprehensive ensemble in QSAR prediction for drug discovery.
BMC Bioinformatics
; 20(1): 521, 2019 Oct 26.
Article
in En
| MEDLINE
| ID: mdl-31655545
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Quantitative Structure-Activity Relationship
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2019
Document type:
Article
Affiliation country:
South Korea
Publication country:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM