Events MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.

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Published

This submission belongs to the session 03. USEDAT-02: USA-Europe Data Analysis Training Program Workshop, Cambridge, UK-Bilbao, Spain-Miami, USA, 2016 of the event MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.

Published date

06 Dec, 2016

Citation

Jesus Vicente de Julián-Ortiz, Lionello Pogliani, Emili Besalú, <strong>Artificial Neural Networks and Multilinear Least Squares to Model Physicochemical Properties of Organic Solvents</strong>, in Proceedings of MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed., 15 October–20 October 2022, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-02-03826

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Artificial Neural Networks and Multilinear Least Squares to Model Physicochemical Properties of Organic Solvents

Jesus Vicente de Julián-Ortiz ¹

Lionello Pogliani ^2,3

Emili Besalú ⁴

1. Departament de Química Física, Universitat de València, Spain

2. Departamento de Química Física, Universidad de Valencia, Spain

3. MOLware SL, C/ Burriana 36, 3, Valencia, Spain

4. Institut de Química Computacional, Universitat de Girona, Spain

Abstract

Keywords

Neural networks

Linear models

QSPR

Mean molecular connectivity indices

State indices

Molecular connectivity

Poster

MOL2NET-02 - Artificial Neural Networks and Multilinear Least Squares to Model Physicochemical Properties of Organic Solvents.pdf