Viviana Consonni
University of Milano-Bicocca
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Viviana Consonni.
Methods and Principles in Medicinal Chemistry | 2000
Roberto Todeschini; Viviana Consonni
Users guide notations acronyms list of molecular descriptors. Appendices: counting and topological descriptors calculation of descriptors tables of molecular descriptor values.
Journal of Medicinal Chemistry | 2014
Artem Cherkasov; Eugene N. Muratov; Denis Fourches; Alexandre Varnek; I. I. Baskin; Mark T. D. Cronin; John C. Dearden; Paola Gramatica; Yvonne C. Martin; Roberto Todeschini; Viviana Consonni; Victor E. Kuz’min; Richard D. Cramer; Romualdo Benigni; Chihae Yang; James F. Rathman; Lothar Terfloth; Johann Gasteiger; Ann M. Richard; Alexander Tropsha
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
Journal of Chemical Information and Computer Sciences | 2002
Viviana Consonni; Roberto Todeschini; 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.
Journal of Chemical Information and Modeling | 2009
Viviana Consonni; Davide Ballabio; Roberto Todeschini
This paper deals with the problem of evaluating the predictive ability of QSAR models and continues the discussion about proper estimates of the predictive ability from an external evaluation set reported in Schüürmann G., Ebert R.-U., et al. External Validation and Prediction Employing the Predictive Squared Correlation Coefficient--Test Set Activity Mean vs Training Set Activity Mean. J. Chem. Inf. Model. 2008, 48, 2140-2145 . The two formulas for calculating the predictive squared correlation coefficient Q2 previously discussed by Schüürmann et al. are one that adopted by the current OECD guidelines about QSAR validation and based on SS (sum of squares) of the external test set referring to the training set response mean and the other based on SS of the external test set referring to the test set response mean. In addition to these two formulas, another formula is evaluated here, based on SS referring to mean deviations of observed values from the training set mean over the training set instead of the external evaluation set.
Journal of Chemical Information and Computer Sciences | 2002
Viviana Consonni; Roberto Todeschini; Manuela Pavan; Paola Gramatica
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 3D-molecular 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.
Chemosphere | 2000
Paola Gramatica; M Corradi; Viviana Consonni
Soil sorption coefficients (K(OC)) of 185 non-ionic organic heterogeneous pesticides have been studied searching for quantitative structure-property relationships (QSPRs). The chemical description of pesticide structure has been made in terms of some molecular descriptors: count descriptors, topological indices, information indices, fragment-based descriptors and weighted holistic invariant molecular (WHIM) descriptors; these last are statistical indices describing size, shape, symmetry and atom distribution of molecules in the three-dimensional space. Three new topological indices derived from the electrotopological state indices of Kier and Hall were proposed. Multiple linear regression analysis was performed after previous selection of the descriptors mostly correlated to the response by Genetic Algorithms. The obtained results confirm the capability of the proposed approach to give predictive models for one of the most important partition properties, such as soil sorption coefficient (K(OC)).
Chemometrics and Intelligent Laboratory Systems | 1999
Roberto Todeschini; Viviana Consonni; A. Maiocchi
Abstract A previous paper introduced a new correlation index, K , and tested it to evaluate the correlation content of a set of multivariate data. This paper presents an extension of the K index theory together with some applications in several fields where chemometrics is commonly encountered. Starting from a correlation measurement, evaluated by the K index theory, it becomes possible (a) to calculate the information content within a set of multivariate data, (b) to give an estimate of data set entropy, (c) to allow variable reduction, preserving the correlation structure in the original data. Moreover, (d) the effect of common scaling procedures on the structure of the original data can be measured, (e) and an estimate made of the minimum number of cross-validation groups, without loosing relevant but not predictable information; finally (f) a search can be made for the best subset models in regression analysis excluding models without predictive power.
Journal of Chemical Information and Modeling | 2013
Kamel Mansouri; Tine Ringsted; Davide Ballabio; Roberto Todeschini; Viviana Consonni
The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from quantitative structure-activity relationship (QSAR) models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modeling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and nonbiodegradable chemicals by means of different mathematical methods: k nearest neighbors, partial least squares discriminant analysis, and support vector machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.
Journal of Chemical Information and Modeling | 2012
Roberto Todeschini; Viviana Consonni; Hua Xiang; John D. Holliday; Massimo Buscema; Peter Willett
This paper reports an analysis and comparison of the use of 51 different similarity coefficients for computing the similarities between binary fingerprints for both simulated and real chemical data sets. Five pairs and a triplet of coefficients were found to yield identical similarity values, leading to the elimination of seven of the coefficients. The remaining 44 coefficients were then compared in two ways: by their theoretical characteristics using simple descriptive statistics, correlation analysis, multidimensional scaling, Hasse diagrams, and the recently described atemporal target diffusion model; and by their effectiveness for similarity-based virtual screening using MDDR, WOMBAT, and MUV data. The comparisons demonstrate the general utility of the well-known Tanimoto method but also suggest other coefficients that may be worthy of further attention.
Environmental Pollution | 2013
Maria Grazia Perrone; Maurizio Gualtieri; Viviana Consonni; L. Ferrero; G Sangiorgi; Eleonora Longhin; Davide Ballabio; Ezio Bolzacchini; Marina Camatini
Particulate matter (PM), a complex mix of chemical compounds, results to be associated with various health effects. However there is still lack of information on the impact of its different components. PM2.5 and PM1 samples, collected during the different seasons at an urban, rural and remote site, were chemically characterized and the biological effects induced on A549 cells were assessed. A Partial Least Square Discriminant Analysis has been performed to relate PM chemical composition to the toxic effects observed. Results show that PM-induced biological effects changed with the seasons and sites, and such variations may be explained by chemical constituents of PM, derived both from primary and secondary sources. The first-time here reported biological responses induced by PM from a remote site at high altitude were associated with the high concentrations of metals and secondary species typical of the free tropospheric aerosol, influenced by long range transports and aging.