Data-driven design of chiral covalent fragments using highthroughput chemoproteomics and machine learning

McCarthy, William J.; Nightingale, Luke; Biggs, George S.; Cawood, Emma E.; Dudley-Fraser, Jane; Werner, Thilo; Riziotis, Ioannis G.; Pillay, Timesh D.; Lambert, Hugues; Pogány, Peter; van der Zouwen, Antonie J.; Pettinger, Jonathan; Boulton, Simon J.; House, David; Skehel, J. Mark; Bush, Jacob T.; Rittinger, Katrin

ChemRxiv.  2026.
DOI: https://doi.org/10.26434/chemrxiv.15002030/v1

A significant barrier in translating biological insights into therapeutic targets is the limited availability of high-quality chemical probes for target validation. Chemoproteomic profiling of covalent small molecules has dramatically accelerated the discovery of ligandable binding sites across the human proteome. However, the limited specificity and selectivity of initial hits often hinders their effectiveness in evaluating the functional consequences of ligand binding. To address this challenge, we developed a data-driven strategy that integrates chemoproteomic profiling of enantiomerically pure pairs of cysteine-targeting electrophilic fragments (enantiopairs) with machine learning (ML) to design fragment libraries optimised for proteome-wide selectivity. ML-guided library evolution produced a second generation enantiopair library markedly enriched in selective and stereospecific interactions relative to the first generation library. This approach identified high-quality enantioselective binding events with 205 cysteines, the majority not previously liganded. These findings establish a general framework for designing covalent fragment libraries to deliver higherquality initial hits.

Deciphering covalent kinase inhibitor binding landscape through structural kinome profiling

Zheng Zhao, Philip E. Bourne

European Journal of Medicinal Chemistry, 312, 2026, 118872

https://doi.org/10.1016/j.ejmech.2026.118872

Significant progress in kinase-targeted drug discovery has been made over the past two decades, with 100 FDA-approved kinase-targeted drugs and a substantial number of bioactive kinase inhibitors under preclinical study. However, given that more than 180 kinases have been implicated in disease, there remains a considerable need for continued kinase-targeted drug discovery. Covalent kinase inhibitors (CKIs) are a class of kinase inhibitors that form covalent interactions with kinase targets, valued for the potential for enhanced selectivity through anchoring nucleophiles. Here, we collate all the kinase structures from the PDB into dedicated structural kinome resources, containing: (i) the kinase domain structure database (6969 PDB structures); (ii) the kinase ligand-binding structure database (6122 PDB structures); and (iii) the kinase-CKI complex structure database (325 PDB structures). With these data, we systematically investigate the binding modes of CKIs, the fingerprint characteristics of kinase-CKI interactions, 21 types of electrophilic warheads, and 64 nucleophilic amino acids distributed in 15 corresponding spatial positions in kinase domains. We also mentioned covalent degraders and multi-warhead CKIs. Together, our results offer a comprehensive structural kinase resource and in-depth insights into CKI binding properties, supporting future kinase-targeted drug design. The databases are freely accessible at https://zhengzhster.github.io/KinaseStructureDatabase/.