The Cmax value is used to apply a variable score to the numerous Sorafenib Raf-1 drugs based on the inherent toxicity of the drug. This will also pre vent bias towards drugs with low IC50s. some drugs may achieve efficacy at higher levels solely based on the drug EC50 values. Construction of the relevant target set In this subsection, we present approaches for selection of a smaller relevant set of targets T from the set of all possible targets Inhibitors,Modulators,Libraries K. The inputs for the algorithms in this subsection are the binarized drug targets and continuous sensitivity score. With the scaled sensitivities, we can develop a fitness function to evaluate the model strength for an arbitrary set of targets. As has been established, for any set of targets T0, drug Si has a unique representation.

This representation can be used to separate the drugs into different bins based on the targets it inhibits under T0. Within each of these bins will be several drugs with identical target profiles but different scaled scores. Let the set of scores in each bin be denoted Y for Sj in an arbitrary bin, and we will assign Inhibitors,Modulators,Libraries to each bin the mean sensitivity score of the bin, E. Denote this value P. Within each bin, we want to mini mize the variation between the predicted sensitivity for the target combination, P, and the experimental sensitivities, Y. This notion is equivalent to mini mizing the inconsistencies of the experimental sensitivity values with respect to the predicted sensitivity values for all known target combinations for any set of targets, which in turn suggests the selected target set effectively explains the mechanisms by which the effective drugs are able to kill cancerous cells.

Numerically, we can calculate the inter bin sensitivity error using the following equation This analysis Inhibitors,Modulators,Libraries has one notable flaw if we attempt to min T bins j bin P ? Y only separate the various drugs into bins based on inter bin sensitivity error, we can create an over fitted Inhibitors,Modulators,Libraries solution by breaking each drug into an individual bin. We take two steps to avoid this. First, we attempt to minimize the number of targets during construction of T0. Second, we incorporate an inconsistency term to account for target behavior that we consider to be biologically inaccurate. To expand on the above point, we consider there are two complementary rules by which kinase targets behave.

Research has shown that the bulk of viable kinase tar gets behave as tumor promoters, proteins whose presence and lack of inhibition is related to the continued survival and growth of a cancerous tumor. These targets essentially have a positive correlation with cancer progression. This For brevity, we will denote the scoring function of a target set Inhibitors,Modulators,Libraries with respect to the binarized EC50 values S and the scaled sensitivity scores Y. As the S and Y sets will be fixed when target set generation begins, we reduce this notation further to. Note that T K where inhibitor Lapatinib K denotes the set of all possible targets.