Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.

The eukaryotic topoisomerase II is involved in several vital processes, such as replication, transcription, and recombination. Many compounds interfering with the catalytic action of this enzyme are efficient in human cancer chemotherapy. We applied a methodology combining molecular modeling and virtual screening techniques to identify human topoisomerase II alphainhibitors. Data from structural biology and enzymatic assays together with a good background on the enzyme mechanism of action were helpful in the approach. A human topoisomerase II alpha model provided an insight into the structural features responsible for the activity of the enzyme. A protocol comprising several substructural and protein structure-based three-dimensional pharmacophore filters enabled the successful retrieving of inhibitors of the enzyme from large databases of compounds, thus validating the approach. A subset of protein structural features required for the enzyme inhibition at the protein-DNA interface were identified and incorporated into the pharmacophore models. Compounds sharing a DNA-intercalating chromophore and a moiety interfering with the protein active site emerged as good inhibitors.

No abstract