A5037660295- Computational Drug Discovery Methods
- Advanced Statistical Methods and Models
- Spectroscopy and Chemometric Analyses
- Fault Detection and Control Systems
- Face and Expression Recognition
- Chemistry and Chemical Engineering
- Environmental Toxicology and Ecotoxicology
- Neural Networks and Applications
- Pesticide Residue Analysis and Safety
- Thermal and Kinetic Analysis
- Click Chemistry and Applications
- Pharmaceutical and Antibiotic Environmental Impacts
- Receptor Mechanisms and Signaling
- Educational Technology and Assessment
- Sensory Analysis and Statistical Methods
- Machine Learning and Data Classification
- Bioinformatics and Genomic Networks
- Plant biochemistry and biosynthesis
- Effects and risks of endocrine disrupting chemicals
- Estrogen and related hormone effects
University of Milano-Bicocca
2013-2016
University of Milan
2016
Background:Humans are exposed to thousands of man-made chemicals in the environment. Some mimic natural endocrine hormones and, thus, have potential be disruptors. Most these never been tested for their ability interact with estrogen receptor (ER). Risk assessors need tools prioritize evaluation costly vivo tests, instance, within U.S. EPA Endocrine Disruptor Screening Program.Objectives:We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity...
REACH regulation demands information about acute toxicity of chemicals towards fish and supports the use QSAR models, provided compliance with OECD principles. Existing models present some drawbacks that may limit their regulatory application. In this study, a dataset 908 was used to develop model predict LC50 96 hours for fathead minnow. Genetic algorithms combined k nearest neighbour method were applied on training set (726 chemicals) resulted in based six molecular descriptors. An...
In this study, a QSAR model was developed from data set consisting of 546 organic molecules, to predict acute aquatic toxicity toward Daphnia magna. A modified k-Nearest Neighbour (kNN) strategy used as the regression method, which provided prediction only for those molecules with an average distance k nearest neighbours lower than selected threshold. The final showed good performance (R(2) and Q(2) cv equal 0.78, ext 0.72). It comprised eight molecular descriptors that encoded information...
Two novel classification methods, called N3 (N-nearest neighbors) and BNN (binned nearest neighbors), are proposed. Both methods inspired by the principles of K-nearest neighbors (KNN) method, being both based on object pairwise similarities. Their performance was evaluated in comparison with nine well-known methods. In order to obtain reliable statistics, several comparisons were performed using 32 different literature data sets, which differ for number objects, variables classes. Results...
Quantitative structure–activity relationship (QSAR) models for predicting acute toxicity to Daphnia magna are often associated with poor performances, urging the need improvement meet REACH requirements. The aim of this study was evaluate accuracy, stability and reliability a previously published QSAR model by means further external validation optimize its performance extension new data as well consensus approach. validated large set molecules then compared ChemProp model, from which most...
The objective of the present work was to compare Reshaped Sequential Replacement (RSR) algorithm with other well‐known variable selection techniques in field Quantitative Structure–Property Relationship (QSPR) modelling. RSR is based on a simple sequential replacement procedure addition several ‘reshaping’ functions that aimed (i) ensure faster convergence upon optimal subsets variables and (ii) reject models affected by chance correlation, overfitting pathologies. In particular, three...
ABSTRACT In this preliminary study, mathematical models based on Quantitative Structure Property Relationships (QSPR) were applied in order to analyze how molecular structure of chloroprene rubber accelerators relates their rheological and mechanical properties. QSPR developed disclose which structural features mainly affect the mechanism vulcanization. such a way can help faster more parsimonious design new curative molecules. Regression calibrated two properties (scorch time optimum cure...