The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Christian Kramer - One of the best experts on this subject based on the ideXlab platform.
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Nonadditivity Analysis.
Journal of chemical information and modeling, 2019Co-Authors: Christian KramerAbstract:We introduce the statistics behind a novel type of SAR analysis named "nonadditivity analysis". On the basis of all pairs of matched pairs within a given data set, the approach analyzes whether the same transformations between related molecules have the same effect, i.e., whether they are additive. Assuming that the Experimental Uncertainty is normally distributed, the additivities can be analyzed with statistical rigor and sets of compounds can be found that show significant nonadditivity. Nonadditivity analysis can not only detect nonadditivity, potential SAR outliers, and sets of key compounds but also allow estimating an upper limit of the Experimental Uncertainty in the data set. We demonstrate how complex SAR features that inform medicinal chemistry can be found in large SAR data sets. Finally, we show how the upper limit of Experimental Uncertainty for a given biochemical assay can be estimated without the need for repeated measurements of the same protein-ligand system.
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matched molecular pair analysis significance and the impact of Experimental Uncertainty
Journal of Medicinal Chemistry, 2014Co-Authors: Christian Kramer, Peter Gedeck, Julian E Fuchs, Steven Whitebread, Klaus R LiedlAbstract:Matched molecular pair analysis (MMPA) has become a major tool for analyzing large chemistry data sets for promising chemical transformations. However, the dependence of MMPA predictions on data constraints such as the number of pairs involved, Experimental Uncertainty, source of the experiments, and variability of the true physical effect has not yet been described. In this contribution the statistical basics for judging MMPA are analyzed. We illustrate the connection between overall MMPA statistics and individual pairs with a detailed comparison of average CHEMBL hERG MMPA results versus pairs with extreme transformation effects. Comparing the CHEMBL results to Novartis data, we find that significant transformation effects agree very well if the Experimental Uncertainty is considered. This indicates that caution must be exercised for predictions from insignificant MMPAs, yet highlights the robustness of statistically validated MMPA and shows that MMPA on public databases can yield results that are very ...
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Limits to molecular matched-pair analysis: the Experimental Uncertainty case.
Journal of Cheminformatics, 2014Co-Authors: Christian Kramer, Klaus R LiedlAbstract:Matched-Molecular Pair (MMP) analysis has recently emerged as a data analysis technique in medicinal chemistry. It quickly gained scientific momentum because it tackles key questions in lead optimization. In contrast to classical global QSAR models that attempt to predict the absolute numbers of ADME (absorption, distribution, metabolism, excretion) and toxicological properties, MMP analyses predict the difference in (bio-) chemical properties that can be expected due to small chemical modifications to lead structures, with a much smaller and well-controlled error than global QSAR models. The power of MMP analysis depends on the number of previously documented similar molecular transformations, whereas the definition of chemical similarity plays a key role: the more generous the definition of similarity of the anchoring region, the more examples are available. The more strict the definition of similarity, the lower the variability and thus the clearer the effect on ADME-Tox parameters, but also the less data pairs will be available [1]. The (bio-) chemical effect and the significance of the results depends on the Experimental Uncertainty (=noise) in the data. There is a clear mathematical association between the noise level and the minimum activity difference necessary for statistical significance. Here we demonstrate how the Experimental Uncertainty and variability[2,3] affect Matched Molecular Pair Analysis. It can be shown that for small sample sizes (Context-specific MMPs), the activity differences have to be very large in order to be statistically significant. A full equation for the estimation of minimum significant activity difference, depending on the number of samples, standard deviation of the measurements and the true variance of the biochemical effect is developed. The influence of consistency of assay setups can directly be quantified via the variability and practical consequences for MMP analysis will be presented.
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the Experimental Uncertainty of heterogeneous public ki data
Journal of Medicinal Chemistry, 2012Co-Authors: Christian Kramer, Tuomo Kalliokoski, Peter Gedeck, Anna VulpettiAbstract:The maximum achievable accuracy of in silico models depends on the quality of the Experimental data. Consequently, Experimental Uncertainty defines a natural upper limit to the predictive performance possible. Models that yield errors smaller than the Experimental Uncertainty are necessarily overtrained. A reliable estimate of the Experimental Uncertainty is therefore of high importance to all originators and users of in silico models. The data deposited in ChEMBL was analyzed for reproducibility, i.e., the Experimental Uncertainty of independent measurements. Careful filtering of the data was required because ChEMBL contains unit-transcription errors, undifferentiated stereoisomers, and repeated citations of single measurements (90% of all pairs). The Experimental Uncertainty is estimated to yield a mean error of 0.44 pKi units, a standard deviation of 0.54 pKi units, and a median error of 0.34 pKi units. The maximum possible squared Pearson correlation coefficient (R2) on large data sets is estimated to...
Hugh W Coleman - One of the best experts on this subject based on the ideXlab platform.
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Verification and Validation in Computational Fluid Dynamics and Heat Transfer Using Experimental Uncertainty Analysis Concepts
Heat Transfer: Volume 3, 2005Co-Authors: Hugh W ColemanAbstract:An approach to verification and validation (V&V) using Experimental Uncertainty analysis concepts to quantify the result of a validation effort is discussed. This is the approach to V&V being drafted by the American Society of Mechanical Engineers (ASME) Performance Test Code Committee, PTC 61: Verification and Validation in Computational Fluid Dynamics and Heat Transfer. The charter of the committee is “Provides procedures for quantifying the accuracy of modeling and simulation in computational fluid dynamics and heat transfer.” The committee is initially focusing its efforts on drafting a standard for V&V in computational fluid dynamics and heat transfer based on the concepts and methods of Experimental Uncertainty analysis. This will leverage the decades of effort in the community of Experimentalists that resulted in the ASME Standard PTC 19.1 “Test Uncertainty” and the ISO international standard “Guide to the Expression of Uncertainty in Measurement.”Copyright © 2005 by ASME
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Asymmetric systematic uncertainties in the determination of Experimental Uncertainty
AIAA Journal, 1996Co-Authors: W G Steele, Robert P Taylor, Paul K. Maciejewski, C. A. James, Hugh W ColemanAbstract:A new method is presented for determining a 95% confidence Uncertainty interval for an Experimental result when some of the measured variables have asymmetric systematic uncertainties. The technique is compared with the approximate method given in the American National Standards Institute/American Society of Mechanical Engineers (ANSUASME) standard on measurement Uncertainty.
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evaluation of correlated bias approximations in Experimental Uncertainty analysis
AIAA Journal, 1996Co-Authors: Kendall Brown, Hugh W Coleman, W G Steele, Robert P TaylorAbstract:A new method to approximate the effect of correlated bias errors in Experimental Uncertainty analysis is presented. This new method is shown to be greatly superior to previously published and historical approximations, especially when bias errors are estimated in terms of a percentage of reading. To establish this method, the estimation of the bias limit for Experimental results determined from measured variables containing hiases from several elemental sources that are partially or wholly correlated is investigated using a Monte Carlo simulation. For each of four sample data reduction equations, the percentage coverage of the Uncertainty limits computed using the new and previous approximate methods is determined for various combinations of elemental bias source correlation. These coverage values are compared with the desired coverage of 95% to see which method is the most consistent. The new method is found to be by far the most consistent method to approximate the effect of correlated bias errors.
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engineering application of Experimental Uncertainty analysis
AIAA Journal, 1995Co-Authors: Hugh W Coleman, W G SteeleAbstract:Publication in late 1993 by the International Organization for Standardization (ISO) of the Guide to the Expression of Uncertainty in Measurement in the name of ISO and six other international organizations has, in everything but name only, established a new international Experimental Uncertainty standard. In this article, an analysis of the assumptions and approximations used in the development of the methods in the ISO guide is presented, and a comparison of the resulting equation with previously published Uncertainty analysis approaches is made. Also discussed are the additional assumptions necessary to achieve the less complex large sample methodology that is recommended in AIAA Standard S-071-1995, Assessment of Wind Tunnel Data Uncertainty, issued in 1995. It is shown that these assumptions are actually less restrictive than those associated with some previously accepted methodologies. The article concludes with a discussion of some practical aspects of implementing Experimental Uncertainty analysis in engineering testing.
Yukihiro Higashi - One of the best experts on this subject based on the ideXlab platform.
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measurements of the isobaric specific heat capacities for trans 1 3 3 3 tetrafluoropropene hfo 1234ze e in the liquid phase
Journal of Chemical & Engineering Data, 2010Co-Authors: Katsuyuki Tanaka, Gen Takahashi, Yukihiro HigashiAbstract:The isobaric specific heat capacity of trans-1,3,3,3-tetrafluoropropene (HFO-1234ze(E)) in the liquid phase was measured using a metal-bellows calorimeter. Twenty-six data points were obtained in the temperature range from (310 to 370) K and pressure range from (2 to 5) MPa. The relative Experimental Uncertainty of the isobaric specific heat capacity was estimated to be 5 %. On the basis of the present data, the correlation of the isobaric specific heat capacity in the liquid phase was formulated as functions of temperature and pressure. The heat capacities of saturated liquid were derived from this correlation by substituting the vapor pressure.
Jeffrey M. Brown - One of the best experts on this subject based on the ideXlab platform.
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The Experimental Foundation Used to Validate a Reduced Order Model for Mistuned Rotors
52nd Aerospace Sciences Meeting, 2014Co-Authors: Anthony N. Palazotto, J.a. Beck, Jeffrey M. BrownAbstract:This paper describes the initial Experimental framework used in implementing the validation procedure for a reduced order model aimed at modeling a geometrically mistuned rotor. Several items attributing to Experimental Uncertainty have been identified and their effects were realized during this process. These uncertainties investigated in this paper are input locations and output locations. This research explored the response variation associated with these input / output combinations. A consequence of the Experimental results provided insight into several techniques to reduce these Experimental uncertainties and response magnitude variations. Reducing these uncertainties produced stable, repeatable Experimental results. These results will later be used to validate the reduced order model in References [1] and [2].
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The Experimental foundation used to validate a reduced order model for mistuned rotors
52nd Aerospace Sciences Meeting, 2014Co-Authors: G.s. Cox, J.a. Beck, Anthony N. Palazotto, Jeffrey M. BrownAbstract:© 2014, American Institute of Aeronautics and Astronautics Inc. All rights reserved.This paper describes the initial Experimental framework used in implementing the validation procedure for a reduced order model aimed at modeling a geometrically mistuned rotor. Several items attributing to Experimental Uncertainty have been identified and their effects were realized during this process. These uncertainties investigated in this paper are input locations and output locations. This research explored the response variation associated with these input/output combinations. A consequence of the Experimental results provided insight into several techniques to reduce these Experimental uncertainties and response magnitude variations. Reducing these uncertainties produced stable, repeatable Experimental results. These results will later be used to validate the reduced order model in References [1] and [2].
Klaus R Liedl - One of the best experts on this subject based on the ideXlab platform.
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matched molecular pair analysis significance and the impact of Experimental Uncertainty
Journal of Medicinal Chemistry, 2014Co-Authors: Christian Kramer, Peter Gedeck, Julian E Fuchs, Steven Whitebread, Klaus R LiedlAbstract:Matched molecular pair analysis (MMPA) has become a major tool for analyzing large chemistry data sets for promising chemical transformations. However, the dependence of MMPA predictions on data constraints such as the number of pairs involved, Experimental Uncertainty, source of the experiments, and variability of the true physical effect has not yet been described. In this contribution the statistical basics for judging MMPA are analyzed. We illustrate the connection between overall MMPA statistics and individual pairs with a detailed comparison of average CHEMBL hERG MMPA results versus pairs with extreme transformation effects. Comparing the CHEMBL results to Novartis data, we find that significant transformation effects agree very well if the Experimental Uncertainty is considered. This indicates that caution must be exercised for predictions from insignificant MMPAs, yet highlights the robustness of statistically validated MMPA and shows that MMPA on public databases can yield results that are very ...
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Limits to molecular matched-pair analysis: the Experimental Uncertainty case.
Journal of Cheminformatics, 2014Co-Authors: Christian Kramer, Klaus R LiedlAbstract:Matched-Molecular Pair (MMP) analysis has recently emerged as a data analysis technique in medicinal chemistry. It quickly gained scientific momentum because it tackles key questions in lead optimization. In contrast to classical global QSAR models that attempt to predict the absolute numbers of ADME (absorption, distribution, metabolism, excretion) and toxicological properties, MMP analyses predict the difference in (bio-) chemical properties that can be expected due to small chemical modifications to lead structures, with a much smaller and well-controlled error than global QSAR models. The power of MMP analysis depends on the number of previously documented similar molecular transformations, whereas the definition of chemical similarity plays a key role: the more generous the definition of similarity of the anchoring region, the more examples are available. The more strict the definition of similarity, the lower the variability and thus the clearer the effect on ADME-Tox parameters, but also the less data pairs will be available [1]. The (bio-) chemical effect and the significance of the results depends on the Experimental Uncertainty (=noise) in the data. There is a clear mathematical association between the noise level and the minimum activity difference necessary for statistical significance. Here we demonstrate how the Experimental Uncertainty and variability[2,3] affect Matched Molecular Pair Analysis. It can be shown that for small sample sizes (Context-specific MMPs), the activity differences have to be very large in order to be statistically significant. A full equation for the estimation of minimum significant activity difference, depending on the number of samples, standard deviation of the measurements and the true variance of the biochemical effect is developed. The influence of consistency of assay setups can directly be quantified via the variability and practical consequences for MMP analysis will be presented.
