A number of in silico methods have been recently applied for searching and designing multi-target compounds. The simplest approach consists in docking the compounds into all the targets independently. Then, only those molecules that show a high score against all the targets at the same times are collected as hit compounds. This approach, however, is quite computationally expensive, particularly when more than two proteins are considered as targets. Moreover, it does not furnish any information on the structural features required for the multi-target potency, thus it is not suitable for the hit optimization process. Several authors circumvented some of these problems by combining pharmacophore models with docking studies. Do to our interest in multi-kinase inhibitor discovery, we decided to derive a multi-kinase pharmacophore model, facing a two stage approach. Firstly, starting from the structures of the ligands we extracted the features of an appropriate multi-TKI scaffold (scaffold pharmacophore). Then, we decorated this scaffold through information derived from the target structures (multi-TKI pharmacophore). The presented methodology for identifying pharmacophore model could be applied also to other interesting pharmacological models for which a multi-target activity would be valuable.
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I have a doubt about the original data. Why do you have discarded mutant PDB structures?
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Thank you in advance
Humbert
thank you very much for your interest in our work.
We discarded mutant PDB structures because we were mainly interested in deriving a pharmacophore model for wild type-multi kinase inhibitors.
Besides, each mutation in the ATP binding site causes a dramatic and peculiar modification of the protein 3D structure. Hence, the risk in considering also the mutant structures was to derive a too simple and usefulness pharmacophore model constituted, for example, only by “Ar1 containing NA”.
Best regards