Implicit scoring

Implicit scoring consists of tracking various behaviors to determine buying intent. Implicit scoring can also consist of inferred data such as IP addresses and so on. With implicit scores, you want to score the activity beised on the value it has to purchasing. 

Here are some common implicit score changers to think about for a lead ... 

Figure 18-2 shows you an example of implicit scoring that scores behaviors based on buying intent. 

Substitution matrices have also been modeled on amino acid or protein structural properties rather than on alignment data (Grantham, 1974 Miyata et al., 1979 Rao, 1987). However, the relationship between scores and target frequencies means that any imaginable set of scores has an implicit set of target frequencies, and obtaining these from the alignment data itself is the most direct approach (Altschul, 1991). 

Data that have an implicit order of magnitude, such as ASA score. 

Knowledge-Based Scoring. Because the forces that govern protein-ligand interactions are so complex, an implicit approach to capture all relevant terms of protein-ligand... 

In knowledge-based functions, the knowledge that is implicitly encoded in the protein-ligand complexes is tried to be captured. These scores are based on the concept of the inverse formulation of the Boltzmann law ... 

Considering the potentially huge search space, Lathrop and Smith report implicit evaluation of up to 6.8 1028 alignments per second [175] (Table 6.1), which compares with about 140 explicit evaluations of the scoring function as observed by Bryant [209]. 

Implicit membrane models have been used successfully in simulation of membrane proteins [9] and in folding studies of membrane-bound peptides [88-90]. Furthermore, applications of implicit membrane models as scoring functions and in MMGB/SA-type free energy calculations for membrane-bound biomolecules are conceivable [91]. 

Finally, there are programs that add water molecules after potential ligand poses are found solely to improve the scoring [114—116]. Only in the first two approaches are the structural predictions influenced directly by the water molecules. Obviously, all three possibilities to include explicit water molecules in docking can modify the score of a ligand in comparison to a mere implicit treatment of solvation and desolvation. In doing so the challenge is to correctly quantify water contributions. 

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