Chemogenomic approach to increase accuracy of QSAR modeling of inhibition activity against five major P450 isoforms
Sergii Novotarskyi, Iurii Sushko, Robert Koerner, Igor V. Tetko · Journal of Cheminformatics · 2013
Cytochromes P450 (CYP) are a superfamily of enzymes, involved in metabolism of xenobiotic compounds.CYP are involved in metabolism of a large amount of drugs, currently present on the market.Therefore, prediction of CYP inhibition activity of small molecules poses an important task, especially in early stage drug discovery, due to high risk of drug-drug interactions.It is estimated that CYP enzymes metabolize over 75% of currently marketed drugs.Of these reactions over 90% are facilitated by CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4.This makes these enzymes particularly interesting targets for in-silico inhibition prediction.Accurate prediction of inhibition activity of small molecules against CYP enzymes is particularly important in the field of personalized medicine discovery.High promiscuity with respect to substrates of the studied cytochromes limits the approach of traditional QSAR methods.Including structural information of the protein is crucial to obtaining predictive models.In this work the modeling is performed on a set of chemogenomic descriptors obtained from protein-ligand complexes.The quality of the descriptors is benchmarked in QSAR modeling of HTS data for human CYP450 inhibition.The calculation of descriptors involves a flexible docking of the molecule to the rigid binding cite of the cytochrome (in this study the AutoDock Vina tool was used).The obtained topranked conformation is then processed to obtain the descriptors.The training sets for the benchmarked models were obtained from PubChem BioAssay database (assays AID410, AID883, AID899, AID884 and AID891 for CYP1A2, 2C9, 2C19, 3A4 and 2D6, respectively).The test