Variableselectioninmultivariatecalibrationbasedonclusteringofvariableconcept

Recentlywehaveproposedanewvariableselectionalgorithm,basedonclusteringofvariableconcept(CLoVA)inclassificationproblem.Withthesameidea,thisnewconcepthasbeenappliedtoaregres-sionproblemandthentheobtainedresultshavebeencomparedwithconventionalvariableselectionstrategiesforPLS.Thebasicideabehindtheclusteringofvariableisthat,theinstrumentchannelsareclusteredintodifferentclustersviaclusteringalgorithms.Then,thespectraldataofeachclusteraresubjectedtoPLSregression.Differentrealdatasets(Cargillcorn,Biscuitdough,ACEQSAR,Soy,andTablet)havebeenusedtoevaluatetheinfluenceoftheclusteringofvariablesonthepredictionper-formancesofPLS.Almostintheallcases,thestatisticalparameterespeciallyinpredictionerrorshowsthesuperiorityofCLoVA-PLSrespecttoothervariableselectionstrategies.Finallythesynergyclusteringofvariable(sCLoVA-PLS),whichisusedthecombinationofcluster,hasbeenproposedasanefficientandmodificationofCLoVAalgorithm.Theobtainedstatisticalparameterindicatesthatvariableclusteringcansplitusefulpartfromredundantones,andthenbasedoninformativecluster;stablemodelcanbereache

Current Mathematical Methods Used in QSAR/QSPR Studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future

Spherical harmonics coeffcients for ligand-based virtual screening of cyclooxygenase inhibitors

Background: Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. Methodology/Principal Findings: We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. Conclusions/Significance: 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort

(Q)SAR Modelling of Nanomaterial Toxicity - A Critical Review

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored. (Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models

Molecular Modeling on Structure-Function Analysis of Human Progesterone Receptor Modulators

Considering the significance of progesterone receptor (PR) modulators, the present study is explored to envisage the biophoric signals for binding to selective PR subtype-A using ligand-based quantitative structure activity relationship (QSAR) and pharmacophore space modeling studies on nonsteroidal substituted quinoline and cyclocymopol monomethyl ether derivatives. Consensus QSAR models (Training set (Tr): nTr=100, R2pred=0.702; test set (Ts): nTs=30, R2pred=0.705, R2m=0.635; validation set (Vs): nVs=40, R2pred=0.715, R2m=0.680) suggest that molecular topology, atomic polarizability and electronegativity, atomic mass and van der Waals volume of the ligands have influence on the presence of functional atoms (F, Cl, N and O) and consequently contribute significant relations on ligand binding affinity. Receptor independent space modeling study (Tr: nTr=26, Q2=0.927; Ts: nTs=60, R2pred=0.613, R2m=0.545; Vs: nVs=84, R2pred=0.611, R2m=0.507) indicates the importance of aromatic ring, hydrogen bond donor, molecular hydrophobicity and steric influence for receptor binding. The structure-function characterization is adjudged with the receptor-based docking study, explaining the significance of the mapped molecular attributes for ligand-receptor interaction in the catalytic cleft of PR-A