Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library

Pharmaceuticals

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

DOI  :  10.3390/ph4091236

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown