title: Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach.
abstract: We aim at predicting the effect of structure and isotopic substitutions on the equilibrium vapour pressure isotope effect of various organic compounds (alcohols, acids, alkanes, alkenes and aromatics) at intermediate temperatures. We attempt to explore quantitative structure property relationships by using artificial neural networks (ANN); the multi-layer perceptron (MLP) and compare the performances of it with multi-linear regression (MLR). These approaches are based on the relationship between the molecular structure (organic chain, polar functions, type of functions, type of isotope involved) of the organic compounds, and their equilibrium vapour pressure. A data set of 130 equilibrium vapour pressure isotope effects was used: 112 were used in the training set and the remaining 18 were used for the test/validation dataset. Two sets of descriptors were tested, a set with all the descriptors: number of(12)C, (13)C, (16)O, (18)O, (1)H, (2)H, OH functions, OD functions, CO functions, Connolly Solvent Accessible Surface Area (CSA) and temperature and a reduced set of descriptors. The dependent variable (the output) is the natural logarithm of the ratios of vapour pressures (ln R), expressed as light/heavy as in classical literature. Since the database is rather small, the leave-one-out procedure was used to validate both models. Considering higher determination coefficients and lower error values, it is concluded that the multi-layer perceptron provided better results compared to multi-linear regression. The stepwise regression procedure is a useful tool to reduce the number of descriptors. To our knowledge, a Quantitative Structure Property Relationship (QSPR) approach for isotopic studies is novel.
authors.affiliation: Aix-Marseille Université, Laboratoire Chimie Environnement, FRE 3416-CNRS, Marseille, France.
authors.forename: Julien
authors.initials: J
authors.lastname: Parinet
authors.affiliation: Université de Nantes, Chimie et Interdisciplinarité: Synthèse, Analyse et Modélisation, UMR 6230-CNRS, Nantes, France.
authors.forename: Maxime
authors.initials: M
authors.lastname: Julien
authors.affiliation: Université de Nantes, Chimie et Interdisciplinarité: Synthèse, Analyse et Modélisation, UMR 6230-CNRS, Nantes, France.
authors.forename: Pierrick
authors.initials: P
authors.lastname: Nun
authors.affiliation: Université de Nantes, Chimie et Interdisciplinarité: Synthèse, Analyse et Modélisation, UMR 6230-CNRS, Nantes, France.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Robins
authors.affiliation: Université de Nantes, Chimie et Interdisciplinarité: Synthèse, Analyse et Modélisation, UMR 6230-CNRS, Nantes, France.
authors.forename: Gerald
authors.initials: G
authors.lastname: Remaud
authors.affiliation: Aix-Marseille Université, Laboratoire Chimie Environnement, FRE 3416-CNRS, Marseille, France. Electronic address: patrick.hohener@univ-amu.fr.
authors.forename: Patrick
authors.initials: P
authors.lastname: Höhener
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D007554:Isotopes / Q000032:analysis*; D016014:Linear Models; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D055548:Vapor Pressure
chemical_list: D007554:Isotopes
keywords: Artificial neural networks; Equilibrium vapour pressure isotope effects; Multi-linear regression; Quantitative structure–property relationship