A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules

Kinases have been the focus of drug discovery programs
for three
decades leading to over 70 therapeutic kinase inhibitors and biophysical
affinity measurements for over 130,000 kinase-compound pairs. Nonetheless,
the precise target spectrum for many kinases remains only partly understood.
In this study, we describe a computational approach to unlocking qualitative
and quantitative kinome-wide binding measurements for structure-based
machine learning. Our study has three components: (i) a Kinase Inhibitor
Complex (KinCo) data set comprising in silico predicted
kinase structures paired with experimental binding constants, (ii)
a machine learning loss function that integrates qualitative and quantitative
data for model training, and (iii) a structure-based machine learning
model trained on KinCo. We show that our approach outperforms methods
trained on crystal structures alone in predicting binary and quantitative
kinase-compound interaction affinities; relative to structure-free
methods, our approach also captures known kinase biochemistry and
more successfully generalizes to distant kinase sequences and compound
scaffolds.