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Similarity Boosted Quantitative Structure–Activity RelationshipA Systematic Study of Enhancing Structural Descriptors by Molecular Similarity
journal contribution
posted on 2013-05-24, 00:00 authored by Tobias Girschick, Pedro
R. Almeida, Stefan Kramer, Jonna StålringThe
concept of molecular similarity is one of the most central in the
fields of predictive toxicology and quantitative structure–activity
relationship (QSAR) research. Many toxicological responses result
from a multimechanistic process and, consequently, structural diversity
among the active compounds is likely. Combining this knowledge, we
introduce similarity boosted QSAR modeling, where we calculate molecular
descriptors using similarities with respect to representative reference
compounds to aid a statistical learning algorithm in distinguishing
between different structural classes. We present three approaches
for the selection of reference compounds, one by literature search
and two by clustering. Our experimental evaluation on seven publicly
available data sets shows that the similarity descriptors used on
their own perform quite well compared to structural descriptors. We
show that the combination of similarity and structural descriptors
enhances the performance and that a simple stacking approach is able
to use the complementary information encoded by the different descriptor
sets to further improve predictive results. All software necessary
for our experiments is available within the cheminformatics software
framework AZOrange.
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data setsMolecular SimilarityThe conceptMany toxicological responses resultsimilarity descriptorsliterature searchrepresentative reference compoundsapproachreference compoundsEnhancing Structural Descriptorscheminformatics software framework AZOrangeQSAR modelingdescriptor setsmultimechanistic process
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