Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation

The kinome-wide virtual profiling
of small molecules with high-dimensional
structure–activity data is a challenging task in drug discovery.
Here, we present a virtual profiling model against a panel of 391
kinases based on large-scale bioactivity data and the multitask deep
neural network algorithm. The obtained model yields excellent internal
prediction capability with an auROC of 0.90 and consistently outperforms
conventional single-task models on external tests, especially for
kinases with insufficient activity data. Moreover, more rigorous experimental
validations including 1410 kinase-compound pairs showed a high-quality
average auROC of 0.75 and confirmed many novel predicted “off-target”
activities. Given the verified generalizability, the model was further
applied to various scenarios for depicting the kinome-wide selectivity
and the association with certain diseases. Overall, the computational
model enables us to create a comprehensive kinome interaction network
for designing novel chemical modulators or drug repositioning and
is of practical value for exploring previously less studied kinases.