Manuela Pavan

Novel molecular descriptors based on a leverage matrix similar to that defined in statistics and usually used for regression diagnostics are presented. This leverage matrix, called Molecular Influence Matrix (MIM), is here proposed as a new molecular representation easily calculated from the spatial coordinates of the molecule atoms in a chosen conformation. The proposed molecular descriptors are called GETAWAY (GEometry, Topology and Atom-Weights AssemblY) as they try to match 3D-molecular geometry provided by the molecular influence matrix and atom relatedness by molecular topology, with chemical information by using different atomic weightings (atomic mass, polarizability, van der Waals volume, and electronegativity, together with unit weights). A first set of molecular descriptors, called H-GETAWAY, is derived by using only the information provided by the molecular influence matrix, while a second set, called R-GETAWAY, combines this information with geometric interatomic distances in the molecule. The prediction ability in structure-property correlations of the new descriptors was tested by analyzing regressions of these descriptors for selected properties of octanes. • Structure/Response correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. Part 2. Applications of the Novel 3D Molecular Descriptors to some QSAR/QSPR Studies. (2002). R.Todeschini, V.Consonni, M.Pavan and P.Gramatica. J.Chem.Inf.Comput.Sci., 42, 3, 693-705. Abstract: In a previous paper the theory of the new molecular descriptors called GETAWAY (GEometry, Topology and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3Dmolecular geometry, atom relatedness and chemical information. In this paper prediction ability in structure-property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors. • Heavy metals variations in some conifers in Valdaosta (western italian Alps) from the 1930 to 2000. (2002). M.Orlandi, M.Pelfini, M.Pavan, M.Santilli, M.Colombini. Microchemical Journal. 73, Issues 1-2, 237-244. Abstract: The high mountain environment is very sensitive to the climatic and ecological variations, that are registered in several natural archives as glaciers or plants. Trees in particular modify their growth, habitus, ring features and spatial distribution in relation with climate and environmental changes. Heavy metals variation in tree rings of Larix decidua have been determined to asses whether this arboreal species can be used as bio-geochemical tracers of heavy metal pollution of the alpine environment. • A Distance Measure between Models: a Tool for Similarity/Diversity Analysis of Model Populations. (2002). R.Todeschini, V.Consonni, and M.Pavan. Chemometrics and Intelligent Laboratory System, Elsevier Science, 70, 55-61. Abstract: In many research fields there is, nowadays, a lot of readily available information, however it needs processing. This is the case of the field of Quantitative Structure-Activity Relationships (QSAR), which exploits several thousand molecular descriptors, and quality control and multivariate calibration where hundreds of spectroscopic signals are easily obtained from spectroscopic methods. Genetic Algorithms, Simulated Annealing, and Tabu Search are some of the methods that are widely used to process available information to find sets of optimal models. In this case the problem that arises is how to compare the selected models. This work proposes a new measure of the distance between two models, and we will demonstrate that this model distance allows clusters of similar models to be found and the most diverse models to be caught in such a way as to preserve maximum information and diversity.