Search results for "quantitative"
showing 10 items of 2409 documents
Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.
2017
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector…
Artificial neural network applied to prediction of fluorquinolone antibacterial activity by topological methods.
2000
A new topological method that makes it possible to predict the properties of molecules on the basis of their chemical structures is applied in the present study to quinolone antimicrobial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. This makes it possible to determine the minimal inhibitory concentration (MIC) of quinolones. Analysis of the results shows that the experimental and calculated values are highly similar. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried …
<strong>Machine Learning and Atom-Based Quadratic Indices for Proteasome Inhibition Prediction </strong>
2015
The atom-based quadratic indices are used in this work together with some machine learning techniques that includes: support vector machine, artificial neural network, random forest and k-nearest neighbor. This methodology is used for the development of two quantitative structure-activity relationship (QSAR) studies for the prediction of proteasome inhibition. A first set consisting of active and non-active classes was predicted with model performances above 85% and 80% in training and validation series, respectively. These results provided new approaches on proteasome inhibitor identification encouraged by virtual screenings procedures. .
Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms.
2020
Abstract The purpose of this study was to determine the acute toxicity in aquatic organisms of one biocidal active substance and six metabolites derived from biocidal active substances and to assess the suitability of available QSAR models to predict the obtained values. We have reported the acute toxicity in sewage treatment plant (STP) microorganisms, in the freshwater microalgae Pseudokirchneriella subcapitata and in Daphnia magna following OECD test methods. We have also identified in silico models for acute toxicity of these trophic levels currently available in widely recognized platforms such as VEGA and the OECD QSAR ToolBox. A total of six, four and two models have been selected fo…
Biopartitioning micellar chromatoraphy to predict blood to lung, blood to liver, blood to fat and blood to skin partition coefficients of drugs
2009
[EN] Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting the partition coefficients from blood to lung, blood to liver. blood to fat and blood to skin is demonstrated. PLS2 and multiple linear regression (MLR) models based on BMC retention data are proposed and compared with other ones reported in bibliography. The proposed models present better or similar descriptive and predictive capability. (C) 2008 Elsevier B.V. All rights r…
Quantitative structure-activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss.
2006
This study has investigated the development of quantitative structure-activity relationships (QSARs) for the toxicity to rainbow trout Onchorhyncus mykiss Walbaum of 75 organophosphorus and carbamate pesticides. The toxicity data were obtained from an openly available toxicological database and were selected to be representative of a single endpoint. A large number of physicochemical and structural descriptors were calculated for the pesticides. QSAR models were developed using multiple linear regression and partial least-squares analyses. Following the removal of a small number of outliers, predictive QSARs were developed on small numbers of mechanistically relevant descriptors. Applying m…
Novel Cancer Chemotherapy Hits by Molecular Topology: Dual Akt and Beta-Catenin Inhibitors
2015
Background and purposeColorectal and prostate cancers are two of the most common types and cause of a high rate of deaths worldwide. Therefore, any strategy to stop or at least slacken the development and progression of malignant cells is an important therapeutic choice. The aim of the present work is the identification of novel cancer chemotherapy agents. Nowadays, many different drug discovery approaches are available, but this paper focuses on Molecular Topology, which has already demonstrated its extraordinary efficacy in this field, particularly in the identification of new hit and lead compounds against cancer. This methodology uses the graph theoretical formalism to numerically chara…
Use of Catalyst in a 3D-QSAR Study of the Interactions between Flavor Compounds and β-Lactoglobulin
2003
This paper reports a 3D-QSAR study using Catalyst software to explain the nature of interactions between flavor compounds and beta-lactoglobulin. A set of 35 compounds, for which dissociation constants were previously determined by affinity chromatography, was chosen. The set was divided into three subsets. An automated hypothesis generation, using HypoGen software, produced a model that made a valuable estimation of affinity and provided an explanation for the lack of correlation previously observed between the hydrophobicity of terpenes and the affinity for the protein. On the basis of these results, it appears that aroma binding to beta-lactoglobulin is caused by both hydrophobic interac…
Bond-extended stochastic and nonstochastic bilinear indices. I. QSPR/QSAR applications to the description of properties/activities of small-medium si…
2010
Bond-extended stochastic and nonstochastic bilinear indices are introduced in this article as novel bond-level molecular descriptors (MDs). These novel totals (whole-molecule) MDs are based on bilinear maps (forms) similar to use defined in linear algebra. The proposed nonstochastic indices try to match molecular structure provided by the molecular topology by using the kth Edge(Bond)-Adjacency Matrix (Ek, designed here as a nonstochastic E matrix). The stochastic parameters are computed by using the kth stochastic edge-adjacency matrix, ESk, as matrix operators of bilinear transformations. This new edge (bond)-adjacency relationship can be obtained directly from Ek and can be considered li…
Use of molecular topology for the prediction of physico-chemical, pharmacokinetic and toxicological properties of a group of antihistaminic drugs
2002
We used molecular connectivity to search mathematical models for predicting physico-chemical (e.g. the partition coefficient, P), pharmacokinetic (e.g. the time of maximum plasma level, and toxicological properties (lethal dose, LD) for a group of antihistaminic drugs. The results obtained clearly reveal the high efficiency of molecular topology for the prediction of these properties. Randomization and cross-validation by use of leave-one-out tests were also performed in order to assess the stability and the prediction ability of the connectivity functions selected.