Graph-Based Feature Selection Approach for Molecular Activity Prediction
Autor
Cerruela García, Gonzalo
Cuevas-Muñoz, José Manuel
García Pedrajas, Nicolás
Editor
American Chemical SocietyFecha
2022Materia
Molecular activityPrediction
QSAR models
Graph-based method
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In the construction of QSAR models for the prediction of molecular
activity, feature selection is a common task aimed at improving the results and
understanding of the problem. The selection of features allows elimination of
irrelevant and redundant features, reduces the effect of dimensionality problems, and
improves the generalization and interpretability of the models. In many feature
selection applications, such as those based on ensembles of feature selectors, it is
necessary to combine different selection processes. In this work, we evaluate the
application of a new feature selection approach to the prediction of molecular activity,
based on the construction of an undirected graph to combine base feature selectors.
The experimental results demonstrate the efficiency of the graph-based method in
terms of the classification performance, reduction, and redundancy compared to the standard voting method. The graph-based
method can be extended to different feature selection algorithms and applied to other cheminformatics problems.