Bin distribution

Table 2 Typical Bin Distribution Used for ChemDBS-3D 3D Screens - ...

Both the selection of classes of appropriate features, and how the bin distribution is accomplished, are the subjects of current research. One recent approach that holds promise is the expansion of 3D feature keys to include pharmacophoric or stereophoric features as surrogates for the macromolecular receptor site. Each of these features is comprised of a collection of three or four atomic environments (selected from a list including hydrogen-bond donors, acceptors, etc.) in association with interatomic distance ranges. Another theme is the introduction of a 3D surface descriptor as the basis for matching. 

To compute densities for such a large number of distributions, reliable and fully automatic density estimators are necessary. The only density estimators fliscussed in the protein litera,ture are histogram estimates. However, these are nonsmooth and thus not suitable for global optimization techniques that c-ombine local and global search. Moreover, for a sample of size n and an optimally chosen bin size, histogram estimates have an accuracy of This is an extremely poor accurar y, far away from the theoretically attainable accuracy of other density estimators. (To reach = Q.l one... 

Distribution of the scoured wool to the cards then occurs. This is often done using a combination of pneumatic and mechanical transport systems, usually with the provision of accumulator bins that hold a few hour s production. Modem cards are usually fed with tower feeds, which automatically control the height of the wool column. 

Structure Training Set Random Distribution Training Set Same as A2, but with only 3 components A1 spectra condensed into 10 bins ... 

An experimental determination of the molecular weight distribution conceptually sorts the polymer molecules into bins, with one bin for each degree of... 

As it turns out, one vendor s material contains almost no particles (0.5%) in the 261-564 /xm class (bin 15) this means that the %-weight results accurately represent the situation. The other vendor s material, however, contains a sizable fraction (typically 5%, maximally 9%) in this largest size class this implies that 1-5% invisible material is in the size class >564 /xm. Evidently then, the size distribution curve for this second material is accurate only on... 

Figure 4.51. Distribution of experimental data. Six experimental formulations (strengths 1, 2, resp. 3 for formulations A, respectively B) were tested for cumulative release at five sampling times (10, 20, 30, 45, respectively 60 min.). Twelve tablets of each formulation were tested, for a total of 347 measurements (13 data points were lost to equipment malfunction and handling errors). The group means were normalized to 100% and the distribution of all points was calculated (bin width 0.5%, her depicted as a trace). The central portion is well represented by a combination of two Gaussian distributions centered on = 100, one that represents the majority of points, see Fig. 4.52, and another that is essentially due to the 10-minute data for formulation B. The data point marked with an arrow and the asymmetry must be ignored if a reasonable model is to be fit. There is room for some variation of the coefficients, as is demonstrated by the two representative curves (gray coefficients in parentheses, h = peak height, s = SD), that all yield very similar GOF-figures. (See Table 3.4.)...

Figure 22.4 Monte Carlo techniques were used to simulate different hypothetical individuals for different instances of the trial design, using variability and uncertainty distributions from the model analysis. The result is a collection of predicted outcomes, shown as a binned histogram (top figure). Success was defined as a difference in end point measurement of X or smaller between drug and comparator. Likelihood of success (shown in the bottom figure as a cumulative probability) for this example (low/medium drug dose and high comparator dose) is seen to be low, about 33%.

Figure 1. Amplitudes of the Fourier coefficients of log(model density, from a multipolar tit to 23 K diffraction data protect [45]. Continuous line m(x) = uniform distribution. Dotted line m(x) = core and valence monopoles. The vertical bar marks the experimental resolution limit 0.463 A. 

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