Parameters, parallel optimization

Abstract High quality leads provide the foundation for the discovery of successful clinical development candidates, and therefore the identification of leads is an essential part of drug discovery. Many factors contribute to the quality of a lead, including biological, physicochemical, ADME, and PK parameters. The identification of high quality leads, which are needed for successful lead optimization, requires the optimization of all of these parameters. Parallel optimization of all parameters is the most efficient way to achieve the goal of lead identification. 

The following examples taken from the literature since 2001 are intended to illustrate the parallel optimization of some or all of the parameters discussed above, and to illustrate how some of the tools described above can aid in lead generation. It is not meant to be an exhaustive survey of the literature. The specific criteria used to define a successful lead identification campaign vary by group, as do the processes used to reach the lead stage. However, there are also many themes that are common to many of the examples below. A summary of the examples described below is contained in Table 2. 

In particular, parallel optimization of affinity/selectivity and pharmacokinetic properties are difficult to achieve, especially if several PK parameters need to be... 

A short presentation of the Consistent Force Field is given, with emphasis on parametrization and optimization of energy function parameters. For best possible calculation of structure, potential energy functions with parameter values optimized on both structural and other properties must be used. Results from optimization with the Consistent Force Field on alkanes and ethers are applied to glucose, gentiobiose, maltose and cellobiose. Comparison is made with earlier and with parallel work. The meaning and use of conformational maps is discussed shortly. 

In the future, computational algorithms will allow the parallel optimization according to the five pharmacokinetic parameters described above [22]. 

Optimization implies that the method moves in a useful direction, at least eventually. In using a local gradient method, one can check this property by evaluating local function values and gradients, both of which should decrease monotonically, at least near the solution. However, during a search for a global minimum, the clues about the multidimensional landscape are more scattered, so it helps to run a number of searches in parallel. The GA is a prime example of a parallel optimization method that searches many regions of parameter space simultaneously. 

The determination of the quality function represents the most time-consuming phase of the parameter set optimization. The high amount of computing-time results directly from the repeatedly calculated vapor-liquid equilibrium conditions. Nevertheless, these computations of the defined quality function F can be performed in parallel mode for all sets of parameters as the optimization algorithm only requires the quahty values calculated for the selections and fresh mutations. Consequently, the use of parallel processors is possible without a great deal of programming and only a very low level of data communications being required. 

The issue of parallel versus sequential synthesis using multimode or monomode cavities, respectively, deserves special comment. While the parallel set-up allows for a considerably higher throughput achievable in the relatively short timeframe of a microwave-enhanced chemical reaction, the individual control over each reaction vessel in terms of reaction temperature/pressure is limited. In the parallel mode, all reaction vessels are exposed to the same irradiation conditions. In order to ensure similar temperatures in each vessel, the same volume of the identical solvent should be used in each reaction vessel because of the dielectric properties involved [86]. As an alternative to parallel processing, the automated sequential synthesis of libraries can be a viable strategy if small focused libraries (20-200 compounds) need to be prepared. Irradiating each individual reaction vessel separately gives better control over the reaction parameters and allows for the rapid optimization of reaction conditions. For the preparation of relatively small libraries, where delicate chemistries are to be performed, the sequential format may be preferable. This is discussed in more detail in Chapter 5. 

The minimum of ssq is near the true values slope= 6 and intercepts20 that were used to generate the data (see Data mxb. m). ssq is continuously increasing for parameters moving away from their optimal values. Analysing that behaviour more closely, we can observe that the valley is parabolic in all directions. In other words, any vertical plane cutting through the surface results in a parabola. In particular, this is also the case for vertical planes parallel to the axes, i.e. ssq versus only one parameter is also a parabola. This is a property of so-called linear parameters. 

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