Machine Learning-Guided Classification of Druggable Pockets and Phylogenetic Druggability Transfer Across the Human Kinome

Abstract

Protein kinases are among the most intensively pursued therapeutic targets in oncology and beyond, yet selectivity remains largely unsolved; around 536 Manning kinase domains share a conserved ATP-binding pocket, making it difficult to target one without hitting others. Allosteric binding modes which exploit conformational states unique to individual kinases or narrow kinase subfamilies offer a principled route to selectivity. Yet the absence of a kinome-wide structural landscape of these pockets limits their translational potential. Here we construct a machine learning-guided structural atlas of 11,945 kinase inhibitor complexes, training an Extra Trees classifier on pocket residue interaction with ligand to assign all seven canonical binding modes and resolve allosteric subclasses with pharmacological precision. Our structural analysis reveals that approximately 303 Manning kinase domains have known inhibitors bound to them, representing ~56.5% of the Manning kinase domain; of these, only 26% (78 kinases) are targeted by non-ATP-competitive allosteric inhibitors, indicating substantial unexplored pharmacological space. Integrating these classifications with the Manning kinome phylogeny, we demonstrate that evolutionary proximity is a statistically significant predictor of shared allosteric pocket accessibility. Because a functionally similar target has similar conformational dynamics and similar druggable pockets, 'phylogenetic druggability transfer' can act as a strong signal for identifying whether a given kinase can be targeted using an allosteric inhibitor or not. In addition, this work repositions the kinome phylogeny as a map of pharmacological opportunity and provides a reusable predictive framework for drug discovery programs - including orthosteric, allosteric, covalent, bifunctional inhibitors, and chemical degrader - across the understudied kinome.