Chemical complexity mapping in QSAR models

Infrared Spectra as Chemical Descriptors for QSAR Models

Infrared spectra (IR) were used as regressors for a number of QSARs and compared with both mechanistically oriented descriptors and heuristic "chemically neutral" descriptors (modified adjacency matrices eigenvalues). IR spectra usually gave results inferior to those obtained with the mechanistically driven descriptors, with one notable exception, and comparable to those obtained by adjacency m...

“Everything Out” Validation Approach for Qsar Models of Chemical Mixtures

Established strategies for validating QSAR models of binary mixtures of chemicals are not applicable to the most challenging case, which is the prediction of binary mixtures created by two compounds not present in the initial training set. In this study, we have addressed this challenge by introducing the “Everything Out” validation strategy where the external sets are deliberately formed by al...

Mapping the complexity of ecological models

We propose to define the complexity of an ecological model as the statistical complexity of the output it produces. This allows for a direct comparison between data and model complexity. Working with univariate time series, we show that this measure ‘blindly’ discriminates among the different dynamical behaviours a model can exhibit. We then search a model parameter space in order to segment it...

3d Qsar Models

Neural networks in building QSAR models.

This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resul...