Abstract
One strategy to potentially improve the success of drug discovery is to apply computational approaches early in the process to select molecules and scaffolds with ideal binding and physicochemical properties. Numerous algorithms and different molecular descriptors have been used for modeling ligand-protein interactions as well as absorption, distribution, metabolism and excretion (ADME) properties. In most cases a single data set has been evaluated with one approach or multiple algorithms that have been compared for a single dataset. These models have been primarily evaluated by leave-one out analysis or boot strapping with groups representing 25-50% of the training set left out of the final model. In a very few examples a test set of molecules not included in the model has been used for an external evaluation. In the present study we have applied Sammon non-linear maps, Support Vector Machines and Kohonen Self Organizing Maps to modeling numerous datasets for ADME properties including human intestinal absorption, blood brain barrier permeability, cytochrome P450 binding, plasma protein binding, P-gp inhibition, volume of distribution and plasma half life.
Keywords: sammon non-linear maps, support vector machines, self-organizing maps, adme, absorption, cytochrome p, volume of distribution, plasma half-life, p-glycoprotein
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