Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound LibraryReport as inadecuate




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Swiss Federal Institute of Technology ETH, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, Wolfgang-Pauli-Str. 10, CH-8093 Zurich, Switzerland



These authors contributed equally to this work.





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Abstract We present a self-organizing map SOM approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation CATS for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 CDK2 and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches. View Full-Text

Keywords: combinatorial chemistry; drug design; in silico pharmacology; kinase inhibitor; multi-component reaction; self-organizing map combinatorial chemistry; drug design; in silico pharmacology; kinase inhibitor; multi-component reaction; self-organizing map





Author: Petra Schneider, Katharina Stutz †, Ladina Kasper †, Sarah Haller, Michael Reutlinger, Felix Reisen, Tim Geppert and Gisbert Schneider *

Source: http://mdpi.com/



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