Variable target function method

Figure 5 Optimization of the objective function in Modeller. Optimization of the objective function (curve) starts with a random or distorted model structure. The iteration number is indicated below each sample structure. The first approximately 2000 iterations coiTespond to the variable target function method [82] relying on the conjugate gradients technique. This approach first satisfies sequentially local restraints, then slowly introduces longer range restraints until the complete objective function IS optimized. In the remaining 4750 iterations, molecular dynamics with simulated annealing is used to refine the model [83]. CPU time needed to generate one model is about 2 mm for a 250 residue protein on a medium-sized workstation.

Rather than regarding the force field as a fixed part of the refinement procedure, it may be quite reasonable to adjust force field parameters to make barriers easier to surmount or to use force field parameters as variables that can be altered to implement a refinement method. In one sense, this principle of minimizing on a changing potential surface could be seen as the heart of the variable target function method discussed previously.8... 

Torsion angle space minimization (variable target function method) 242... 

Current practices in industrial pharmacy can now be put in perspective. Typically, the method of choice is univariate one variable at a time (OVAT). One variable is examined for a few conditions, which, in practice, are selected within a safe subset of the permissible design space. A value of this parameter is selected and kept subsequently constant. Another variable is then examined, a value is chosen, and the process continues sequentially. Intuitively, unless the target function is essentially a plane, if the end result is anywhere near the global optimum, it is only by chance. A historical reason for this dated practice is that the regulatory framework greatly discouraged implementation of the virtuous cycle mentioned above, which... 

Essentially, the Hamiltonian controlling mechanism is foimded on the basis of the value method. When using this approach by understanding the physical-chemical and kinetic properties, unavoidably this results into two new conjugate variables to describe the kinetic system, whereby the value of chemical species and the individual steps are specified, which characterize the kinetic system s significance via the target functional of the selected reaction. 

In the response surface strategy that was discussed in Section 2.3 standard response surface techniques are used to generate two response surface models, one for the mean response and one for the standard deviation of the response (or some function of the standard deviation). The standard deviation measures the stability of the response to the environmental variation. Standard analysis can reveal which factors affect the mean only, which only affect the variability, and which affect both the mean and the variability. The researcher can then apply optimization methods or construct contour plots of the mean and standard deviation response surfaces to determine settings of the design variables that will give a mean response that is close to the target with minimum variation. 

MODIFICATION OF THE METHOD Data from individuals drawn from a target population are not completely independent. Concentration time curves (longitudinal data) of a subject are considered to be driven by a functionality depending on individual parameter values. But what is the connection between the same parameters in different persons Parts of it may be described by a functionality depending on demographic variables. In any case, unexplained intra and inter individual random effects remain. Mixed effect modeling clearly distinguishes between these two sources of randomness. 

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