The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform

Characterizing the kinome selectivity profiles of kinase
inhibitors
is essential in the early stages of novel small-molecule drug discovery.
This characterization is critical for interpreting potential adverse
events caused by off-target polypharmacology effects and provides
unique pharmacological insights for drug repurposing development of
existing kinase inhibitor drugs. However, experimental profiling of
whole kinome selectivity is still time-consuming and resource-demanding.
Here, we report a deep learning classification model using an in-house
built data set of inhibitors against 191 well-representative kinases
constructed based on a novel strategy by systematically cleaning and
integrating six public data sets. This model, a multitask deep neural
network, predicts the kinome selectivity profiles of compounds with
novel structures. The model demonstrates excellent predictive performance,
with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92,
0.90, and 0.37, respectively. It also performs well in a priori testing
for inhibitors targeting different categories of proteins from internal
compound collections, significantly improving over similar models
on data sets from practical application scenarios. Integrated to subsequent
machine learning-enhanced virtual screening workflow, novel CDK2 kinase
inhibitors with potent kinase inhibitory activity and excellent kinome
selectivity profiles are successfully identified. Additionally, we
developed a free online web server, KinomePro-DL, to predict the kinome
selectivity profiles and kinome-wide polypharmacology effects of small
molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users
to quickly fine-tune it with their own training data sets, enhancing
both prediction accuracy and robustness.