Premium
A Novel Strategy of Structural Similarity Based Consensus Modeling
Author(s) -
Lei Beilei,
Li Jiazhong,
Yao Xiaojun
Publication year - 2013
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200170
Subject(s) - similarity (geometry) , computer science , set (abstract data type) , selection (genetic algorithm) , artificial intelligence , quantitative structure–activity relationship , population , data mining , machine learning , model selection , image (mathematics) , demography , sociology , programming language
Abstract A novel strategy of “structural similarity based consensus modeling” (SSCM) based on “model distance andguided model selection” (MD‐QGMS) submodel set was proposed. The SSCM strategy is built upon a hypothesis, that is, similar compounds are most probably predicted more accurately by a same submodel among a model population, which can be concluded from the fact that models employing a different set of descriptors can predict compounds with specific structures more accurately. It is proved that the proposed SSCM strategy can remarkably improve the external prediction ability of QSAR models by employing two different datasets. In future, the proposed SSCM strategy may provide a new direction to develop more accurate predictive models.