Effects of Molecular Representation in Predicting the Biological Activity using SVM and PLS Approaches
![Thumbnail](/xmlui/bitstream/handle/10396/17331/P%c3%a1ginas%20desdeactas.pdf.jpg?sequence=4&isAllowed=y)
View/ Open
Author
Cerruela García, Gonzalo
Luque Ruiz, Irene
García Pedrajas, Nicolás
Gómez-Nieto, Miguel Ángel
Date
2014Subject
Molecular activity predictionQSAR models
SVM
PLS regression
METS:
Mostrar el registro METSPREMIS:
Mostrar el registro PREMISMetadata
Show full item recordAbstract
In this work we study and analyze the behavior
of different representational spaces
for molecular activity prediction. Representational
spaces based on fingerprint similarity,
structural similarity using maximum common
subgraphs (MCS) and all maximum common
subgraphs (AMCS) approaches are compared
against representational spaces based on structural
fragments and non-isomorphic fragments
(NIF), built using different molecular descriptors.
Support vector machine is used to study
the influence of molecular representation in the
dataset classification and PLS regression is proposed
to construct a QSAR model for the molecular
activity prediction