Besides target-independent approaches, we focus our structure-based developments on kinases, one of the major classes of therapeutic targets. This includes fragment-based approaches, kinase binding site comparison, and others.
The KinFragLib project allows to explore and extend the chemical space of kinase inhibitors using data-driven fragmentation and recombination, built on available structural kinome data from the KLIFS database for over 2,500 kinase complexes. The computational fragmentation method splits known non-covalent kinase inhibitors into fragments with respect to their 3D proximity to six predefined subpockets relevant for binding.
The majority of signal transduction in eukaryotic cells is mediated by protein kinases, whereas dysregulation is often associated with cancer, making them an important drug target. Fragment-based drug design strategies have already shown promising results for developing novel kinase inhibitors. Typically, these approaches consider the structural information about the target but disregard the pre-existing knowledge concerning kinase ligands, although the kinase binding pocket is highly conserved and well-studied.
In this project, we work with the protein kinase family, since they are involved in many diseases which make them an interesting drug target. The aim is to generate novel ligands as potential drug candidates for kinases using generative deep learning. The model includes structural information on the kinase binding pocket, since it is well studied and well conserved across protein kinases. We tackle the problem of ligand generation in a fragment-based approach by including domain-knowledge to increase kinase affinity and synthesizability in general.