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Using the solution by Tam of Navier-Stokes equations for creeping flow around an active sphere surrounded by a random cloud of inactive spheres, an asymptotic solution of the convective diffusion equation is obtained for high Schmidt numbers.
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CCX771, but not its inactive analogue CCX704, inhibited sphere formation in a dose-dependent manner.
Upon further investigation, it was found that 141 of the 157 targets (89.8 %) that score precision and recall scores above 0.97 belong to targets comprised of sphere excluded (SE) inactive compounds.
In some cases, we added manually exclusion volume spheres to remove decoys compounds since inactive compounds can map all the pharmacophore require features, their inactivity being explained by steric clashes with the binding site [35, 36].
To balance the class sizes, a sphere exclusion algorithm was used to extract putative inactive data.
For some LB pharmacophores, we manually added exclusion volume spheres to remove decoys compounds using the hypothesis that if inactive compounds map all the require features, their inactivity can be due to steric clashes with the binding site [35, 36].
During internal validation, sphere exclusion models perform better in comparison to PubChem inactive classes, due to the dissimilarity requirement between the active compounds and SE inactive compounds.
Initial models were built in MODELLER using a flexible sphere of 5 Å around mutated residue and the inactive crystal structures of the WT EGFR, FLT3, and KIT kinases as the templates.
A sphere exclusion (SE) algorithm was utilized to randomly sample 'presumed inactive' compounds from a pool of ChEMBL compounds using a dissimilarity radius from known actives.
The inner sphere corresponds to the actual endpoint activity, with class values active (red) and inactive (blue).
She never understood inactive.
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