Feature Selection and Polydispersity Characterization for QSPR Modelling: Predicting a Tensile Property (8 pag)

FIORELLA CRAVERO; SCHUSTIK, SANTIAGO; MARÍA JIMENA MARTÍNEZ; BARRANCO, CARLOS D.; MONICA F. DIAZ; IGNACIO PONZONI

Toledo

Conferencia; PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics; 2018

University of Sevilla, Spain - University of Vigo, Spain

https://doi.org/10.1007/978-3-319-98702-6_6 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.