CustomKinFragLib: Filtering the Kinase-Focused Fragmentation Library

Protein kinases play a crucial role in key regulatory cell processes and are known to be dysregulated in diseases such as cancer and autoimmune disorders. Hence, protein kinases represent a vital drug target class. To meet the challenge of designing novel kinase inhibitors, fragment-based drug discovery (FBDD) has already shown great promise. The kinase-specific fragment library KinFragLib is a data-driven FBDD approach providing a powerful subpocket-specific framework for creating potentially feasible kinase inhibitors through subpocket-guided enumeration and combination of fragments. However, traversing the whole recombination space is computationally infeasible. Here, we present CustomKinFragLib, a fragment library reduction pipeline that builds onto KinFragLib. CustomKinFragLib considers several drug-relevant aspects, including synthesizability by filtering according to commercially available building blocks, calculating a synthetic accessibility score, and filtering for fragments with available retrosynthetic pathways. It also considers molecular properties often associated with drug-likeness and removes fragments containing unwanted substructures. Our new fragmentation library reduces KinFragLib from 9131 to 523 fragments while retaining diverse fragments with drug-like properties and high synthetic tractability. The code and dataset are available at https://github.com/volkamerlab/KinFragLib.