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.
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Acknowledgments
This work is kindly supported by CONICET, grant PIP 112-2012-0100471 and UNS, grants PGI 24/N042 and PGI 24/ZM17. This work has been also partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under the project TIN2015-64776-C3-2-R DIFERENTIAL@UPO: Massive data management, filtering and exploratory analysis. We also thank to SGPEC of UNS for partially supported the visit of Dr. Barranco to the ICIC in 2016 and to the AUIP (Asociación Universitaria Iberoamericana de Postgrado) for partially supported the visit of Dr. Ponzoni to the Pablo de Olavide University in 2017.
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Cravero, F., Schustik, S., Martínez, M.J., Barranco, C.D., Díaz, M.F., Ponzoni, I. (2019). Feature Selection and Polydispersity Characterization for QSPR Modelling: Predicting a Tensile Property. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., González, P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-319-98702-6_6
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DOI: https://doi.org/10.1007/978-3-319-98702-6_6
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