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Model Generation (model + generation)

Distribution by Scientific Domains

Chemistry	60%

Selected Abstracts

A centroid-based sampling strategy for kriging global modeling and optimization

AICHE JOURNAL, Issue 1 2010
Eddie Davis
Abstract A new sampling strategy is presented for kriging-based global modeling. The strategy is used within a kriging/response surface (RSM) algorithm for solving NLP containing black-box models. Black-box models describe systems lacking the closed-form equations necessary for conventional gradient-based optimization. System optima can be alternatively found by building iteratively updated kriging models, and then refining local solutions using RSM. The application of the new sampling strategy results in accurate global model generation at lower sampling expense relative to a strategy using randomized and heuristic-based sampling for initial and subsequent model construction, respectively. The new strategy relies on construction of an initial kriging model built using sampling data obtained at the feasible region's convex polytope vertices and centroid. Updated models are constructed using additional sampling information obtained at Delaunay triangulation centroids. The new sampling algorithm is applied within the kriging-RSM framework to several numerical examples and case studies to demonstrate proof of concept. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

Model-building strategies for low-resolution X-ray crystallographic data

ACTA CRYSTALLOGRAPHICA SECTION D, Issue 2 2009
Anjum M. Karmali
The interpretation of low-resolution X-ray crystallographic data proves to be challenging even for the most experienced crystallographer. Ambiguity in the electron-density map makes main-chain tracing and side-chain assignment difficult. However, the number of structures solved at resolutions poorer than 3.5,Å is growing rapidly and the structures are often of high biological interest and importance. Here, the challenges faced in electron-density interpretation, the strategies that have been employed to overcome them and developments to automate the process are reviewed. The methods employed in model generation from electron microscopy, which share many of the same challenges in providing high-confidence models of macromolecular structures and assemblies, are also considered. [source]

Robust Isolation Of Sensor Failures

ASIAN JOURNAL OF CONTROL, Issue 1 2003
R. Xu
ABSTRACT Sensor self-validity check is a critical step in system control and fault diagnostics. In this paper, a robust approach to isolate sensor failures is proposed. First, a residual model for a given system is built off-line and directly based on input-output measurement data. The residual model outputs are called "primary residuals" and are zero when there is no fault. Most conventional approaches to residual model generation are indirect, as they first require the determination of state-space or other models using standard system identification algorithms. Second, a new max-min design of structured residuals, which can maximize the sensitivity of structured residuals with respect to sensor failures, is proposed. Based on the structured residuals, one can then isolate the sensor failures. This design can also be done in an off-line manner. It is an optimization procedure that avoids local optimal solutions. Simulation and experimental results demonstrated the effectiveness of the proposed method. [source]

Investigation of protein binding affinity and preferred orientations in ion exchange systems using a homologous protein library

BIOTECHNOLOGY & BIOENGINEERING, Issue 3 2009
Wai Keen Chung
Abstract A library of cold shock protein B (CspB) mutant variants was employed to study protein binding affinity and preferred orientations in cation exchange chromatography. Single site mutations introduced at charged amino acids on the protein surface resulted in a homologous protein set with varying charge density and distribution. The retention times of the mutants varied significantly during linear gradient chromatography. While the expected trends were observed with increasing or decreasing positive charge on the protein surface, the degree of change was a strong function of the location and microenvironment of the mutated amino acid. Quantitative structure,property relationship (QSPR) models were generated using a support vector regression technique that was able to give good predictions of the retention times of the various mutants. Molecular descriptors selected during model generation were used to elucidate the factors affecting protein retention. Electrostatic potential maps were also employed to provide insight into the effects of protein surface topography, charge density and charge distribution on protein binding affinity and possible preferred binding orientations. The use of this protein mutant library in concert with the qualitative and quantitative analyses presented in the article provides an improved understanding of protein behavior in ion exchange systems. Biotechnol. Bioeng. 2009; 102: 869,881. © 2008 Wiley Periodicals, Inc. [source]

Prospective Validation of a Comprehensive In silico hERG Model and its Applications to Commercial Compound and Drug Databases

CHEMMEDCHEM, Issue 5 2010
Munikumar
Abstract Ligand-based in silico hERG models were generated for 2,644 compounds using linear discriminant analysis (LDA) and support vector machines (SVM). As a result, the dataset used for the model generation is the largest publicly available (see Supporting Information). Extended connectivity fingerprints (ECFPs) and functional class fingerprints (FCFPs) were used to describe chemical space. All models showed area under curve (AUC) values ranging from 0.89 to 0.94 in a fivefold cross-validation, indicating high model consistency. Models correctly predicted 80,% of an additional, external test set; Y-scrambling was also performed to rule out chance correlation. Additionally models based on patch clamp data and radioligand binding data were generated separately to analyze their predictive ability when compared to combined models. To experimentally validate the models, 50 of the predicted hERG blockers from the Chembridge database and ten of the predicted non-hERG blockers from an in-house compound library were selected for biological evaluation. Out of those 50 predicted hERG blockers, tested at a concentration of 10,,M, 18 compounds showed more than 50,% displacement of [3H]astemizole binding to cell membranes expressing the hERG channel. Ki values of four of the selected binders were determined to be in the micromolar and high nanomolar range (Ki (VH01)=2.0,,M, Ki (VH06)=0.15,,M, Ki (VH19)=1.1,,M and Ki (VH47)=18 ,M). Of these four compounds, VH01 and VH47 showed also a second, even higher affinity binding site with Ki values of 7.4,nM and 36,nM, respectively. In the case of non-hERG blockers, all ten compounds tested were found to be inactive, showing less than 50,% displacement of [3H]astemizole binding at 10,,M. These experimentally validated models were then used to virtually screen commercial compound databases to evaluate whether they contain hERG blockers. 109,784 (23,%) of Chembridge, 133,175 (38,%) of Chemdiv, 111,737 (31,%) of Asinex and 11,116 (18,%) of the Maybridge database were predicted to be hERG blockers by at least two of the models, a prediction which could, for example, be used as a pre-filtering tool for compounds with potential hERG liabilities. [source]