Publications

@conference{Cravero2018,

title = {Feature Selection and Polydispersity Characterization for QSPR Modelling: Predicting a Tensile Property},

author = {F. Cravero and S. Schustik and M. J. Martínez and C. D. Barranco and M. F. Díaz and I. Ponzoni},

doi = {10.1007/978-3-319-98702-6_6},

isbn = {978-3-319-98702-6},

year  = {2018},

date = {2018-08-17},

booktitle = {Practical Applications of Computational Biology and Bioinformatics, 12th International Conference},

pages = {43-51},

abstract = {QSPR (Quantitative Structure-Property Relationship) models proposed in Polymer Informatics typically use reduced computational representations of polymers for avoiding the complex issues related with the polydispersion of these industrial materials. In this work, the aim is to assess the effect of this oversimplification in the modelling decisions and to analyze strategies for addressing alternative characterizations of the materials that capture, at least partially, the polydispersion phenomenon. In particular, a cheminformatic study for estimating a tensile property of polymers is presented here. Four different computational representations are analyzed in combination with several machine learning approaches for selecting the most relevant molecular descriptors associated with the target property and for learning the corresponding QSPR models. The obtained results give insight about the limitations of using oversimplified representations of polymers and contribute with alternative strategies for achieving more realistic models.},

keywords = {Artificial intelligence, Feature selection},

pubstate = {published},

tppubtype = {conference}

}