Intelligent systems for predictive modelling in cheminformatics: QSPR models for material design using machine learning and visual analytics tools

F. Cravero, M. J. Martinez, G. E. Vazquez, M. F. Díaz, I. Ponzoni

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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Resumen

In this paper, the use of intelligence systems for feature extraction in predictive modelling applied to Cheminformatics is presented. In this respect, the application of these methods for predicting mechanical properties related to the design of the polymers constitutes, by itself, a central contribution of this work, given the complexity of in silico studies of macromolecules and the few experiences reported in this matter. In particular, the methodology evaluated in this paper uses a features learning method that combines a quantification process of 2D structural information of materials with the autoencoder method. Several inferred models for tensile strength at break, which is a mechanical property of materials, are discussed. These results are contrasted to QSPR models generated by traditional approaches using accuracy metrics and a visual analytic tool.

Idioma originalInglés
Título de la publicación alojada10th International Conference on Practical Applications of Computational Biology and Bioinformatics
EditoresFlorentino Fdez-Riverola, Juan F. De Paz, Miguel P. Rocha, Francisco J. Domínguez Mayo, Mohd Saberi Mohamad
EditorialSpringer Verlag
Páginas3-11
Número de páginas9
ISBN (versión impresa)9783319401256
DOI
EstadoPublicada - 2016
EventoInternational Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016 - Sevilla
Duración: 1 jun. 20163 jun. 2016

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen477
ISSN (versión impresa)2194-5357

Conferencia

ConferenciaInternational Conference on Practical Applications of Computational Biology and Bioinformatics PACBB, 2016
País/TerritorioSpain
CiudadSevilla
Período1/06/163/06/16

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