Davide Ballabio

TL;DR: Novel in silico models to identify organic AR modulators in the context of the Collaborative Modeling Project of Androgen Receptor Activity are described, based on a consensus of a multivariate Bernoulli Naive Bayes, a Random Forest, and N-Nearest Neighbor classification models.

TL;DR: New Quantitative Structure‐Activity Relationship (QSAR) models for the prediction of very toxic and nontoxic endpoints were developed and demonstrated to be robust and predictive, as determined by a blind validation on a set of external molecules provided in a later stage by the coordinators of the collaborative project.

TL;DR: Final results seem to indicate that the application of CAIMAN to the geographical origin identification offers several advantages: first, it shows--on an average basis--good performances; second, it is able to deal in a simple way classification problems related to tipicity, authenticity, and uniqueness characterization, which are of increasing interest in food quality issues.

TL;DR: In this work, acoustic-mechanical properties of sliced toasted breads were analysed by means of chemometric tools, such as Principal Component Analysis and Discriminant Analysis, and proved that multivariate analysis is able to extract relevant information and offer an easy and promising approach for the interpretation of instrumental food texture attributes.

TL;DR: A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity, using Dragon molecular descriptors and genetic algorithms to select descriptors better correlated with toxicity data.