Modeling Kinase Inhibition Using Highly Confident Data Sets

Protein kinases form a consistent
class of promising drug targets,
and several efforts have been made to predict the activities of small
molecules against a representative part of the kinome. This study
continues our previous work (Bora, A.; Avram, S.; Ciucanu, I.; Raica, M.; Avram, S. Predictive Models for Fast and Effective Profiling
of Kinase Inhibitors. J. Chem. Inf. Model. 2016, 56, 895−905; www.chembioinf.ro) aiming to build and measure the performance of ligand-based kinase
inhibitor prediction models. Here we analyzed kinase–inhibitor
pairs with multiple activity points extracted from the ChEMBL database
and identified the main sources of inconsistency. Our results indicate
that lower IC50 values are usually less affected by errors
and reflect more accurately the structure–activity relationship
of the molecules against the target, ideally for quantitative structure–activity
relationship studies. Further, we modeled the activities of 104 kinases
using unbiased target-specific activity points. The performance of
predictors built on extended connectivity fingerprints (ECFP4) and
two-dimensional pharmacophore fingerprints (PFPs) are compared by
means of tolerance intervals (TIs) (95%/95%) in virtual screening
(VS) and classification tasks using external random (RandSets) and diversity-based (DivSets) test sets. We found
that the two encodings perform superior to each other on different
kinases in VS and that PFP models perform consistently better in classifying
actives (higher sensitivity). Next, we combined the two encodings
into a single one (PFPECFP) and demonstrated that especially in VS
(as indicated by the exponential receiver operating curve enrichment
metric (eROCE)), for the vast majority of kinases the model performance
increased compared with the individual fingerprint models. These findings
are highlighted in the more challenging DivSets compared
with RandSets. The current paper explores the boundaries
of inhibitor predictors for individual kinases to enhance VS and ultimately
aid the discovery of novel compounds with desirable polypharmacology.