Up to fourth‐generation neutral phosphorus‐containing dendrimers are prepared in good yields in the synthesis outlined here. The only byproducts are NaCl and H2O, and the products contain highly reactive functional groups along the periphery. The first‐generation dendrimer is shown on the right.
The aim of this article is to present the design of a large heterogeneous CNS library (approximately 1700 compounds) from WDI and mapping CNS drugs using QSAR models of blood-brain barrier (BBB) permeation and P-gp substrates. The CNS library finally includes 1336 BBB-crossing drugs (BBB+), 259 molecules non-BBB-crossing (BBB-), and 91 P-gp substrates (either BBB+ or BBB-). Discriminant analysis and PLS-DA have been used to model the passive diffusion component of BBB permeation and potential physicochemical requirement of P-gp substrates. Three categories of explanatory variables (Cdiff, BBBpred, PGPpred) have been suggested to express the level of permeation within a continuous scale, starting from two classes data (BBB+/BBB-), allowing that the degree to which each compound belongs to an activity class is given using a membership score. Finally, statistical data analyses have shown that some very simple descriptors are sufficient to evaluate BBB permeation in most cases, with a high rate of well-classified drugs. Moreover, a "CNS drugs" map, including P-gp substrates and accurately reflecting the in vivo behavior of drugs, is proposed as a tool for CNS drug virtual screening.
This paper reports a comparison of calculated molecular properties and of 2D fragment bit-strings when used for the selection of structurally diverse subsets of a file of 44295 compounds. MaxMin dissimilarity-based selection and k-means clusterbased selection are used to select subsets containing between 1% and 20% of the file. Investigation of the numbers of bioactive molecules in the selected subsets suggest: that the MaxMin subsets are noticeably superior to the k-means subsets; that the property-based descriptors are marginally superior to the fragment-based descriptors; and that both approaches are noticeably superior to random selection.
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