CustomKinFragLib: Filtering the Kinase-Focused Fragmentation Library

Abstract

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 introduce CustomKinFragLib, a curation-focused and user-oriented pipeline that builds on the existing KinFragLib framework. Building on the underlying fragmentation methodology, CustomKinFragLib contributes a systematic post hoc reduction and filtering strategy to generate a smaller, more tractable, and synthesis-friendly fragment set. The pipeline integrates literature-derived drug-relevant filters, including assessments of synthetic accessibility, matching to commercially available building blocks, and availability of retrosynthetic pathways. It also considers molecular properties often associated with drug-likeness and removes fragments containing unwanted substructures. Applying these curated filters reduces the original KinFragLib from 9131 to 837 fragments while retaining diverse fragments with drug-like properties and high synthetic tractability, and providing a practical fragment set suitable for downstream design workflows. Our pipeline is easily customizable, allowing for modifications or exclusion of filters based on the user’s preferences. The code and data set are available at https://github.com/volkamerlab/KinFragLib.