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[01005] A mixed ligand – Autogrid based pharmacophore model for the rational design of multi-kinase inhibitors

1 Department of Pharmacy, University of Naples Federico II, via Montesano 49, 80131 Naples, Italy
2 Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy.
* Author to whom correspondence should be addressed.
14 November 2016
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Abstract

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.

Keywords

kinase inhibitors;pharmacophore; autogrid; multi-targeting compounds

Cite this article as

Di Giovanni, C.; Marzaro, G. A mixed ligand – Autogrid based pharmacophore model for the rational design of multi-kinase inhibitors . In Proceedings of the MOL2NET, International Conference on Multidisciplinary Sciences, 25 December 2016–25 January 2017; Sciforum Electronic Conference Series, Vol. 2, 2016 , 01005; doi:10.3390/mol2net-02-01005

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