Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains
many promising drug targets.
Many kinase inhibitors target the ATP-binding pocket, leading to approved
drugs in past decades. Scaffold hopping is an effective approach for
drug design. The kinase ATP-binding pocket is highly conserved, crossing
the whole kinase family. This provides an opportunity to develop a
scaffold hopping approach to explore diversified scaffolds among various
kinase inhibitors. In this work, we report the SyntaLinker-Hybrid
scheme for kinase inhibitor scaffold hopping. With this scheme, we
replace molecular fragments bound at the conserved kinase hinge region
with deep generative models. Thus, we are able to generate new kinase-inhibitor-like
structures hybridizing the privileged fragments against the hinge
region. We demonstrate that this scheme allows generation of kinase-inhibitor-like
molecules with novel scaffolds, while retaining the binding features
of existing kinase inhibitors. This work can be employed in lead identification
against kinase targets.