Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design

VAZQUEZ, GUSTAVO E.; MARTINEZ, MARÍA JIMENA; PONZONI, IGNACIO; CRAVERO, FIORELLA; DIAZ, MÓNICA F.

Journal of Integrative Bioinformatics

Gruyter

Lugar: Berlin; Año: 2016 vol. 13 p. 216 - 231

1613-4516

Several feature extraction approaches for QSPR modelling in Cheminformatics arediscussed in this paper. In particular, this work is focused on the use of these strategies for predicting mechanical properties, which are relevant for the design of polymeric materials. The methodology analysed in this study employs a feature learning method that uses a quantification process of 2D structural characterization of materials with the autoencoder method. Alternative QSPR models inferred for tensile strength at break (a well-known mechanical property of polymers) are presented. These alternative models are contrasted to QSPR models obtained by feature selection technique by using accuracy measures and a visual analytic tool. The results show evidence about the benefits of combining featurelearning approaches with feature selection methods for the design of QSPR models.