What happened
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. Update (2026-09-21): Two Comparators May Be All We Need . A compound in a cell meets a spectrum of proteins drawn from many families at once, while screening most often interrogates one target at a time. Two questions asked many times in a rank ordering workflow ultimately guide decisions on what gets made and what gets counter-screened, and both are comparative: which of two targets does a compound prefer, and which of two compounds does a target prefer. We built one model for each, over a roster of 1,879 human proteins covering 34 protein families. Each model is given two chemical structures and a sequence, or two sequences and a chemical structure, and returns which member of the pair is preferred together with how firmly it holds that view. No conformational analysis, protein structure, binding site or docked pose is used. Across families, asked which of two targets a compound prefers, the model is correct 0.75 of the time over 8,689 held-out comparisons, and 0.93 of the time on the third of them it holds most confidently. Asked which of two compounds a single target prefers, it is correct 0.71 of the time over 65,725 held-out comparisons, rising to 0.96 on the most confi Update (2026-09-22): GO-Term Enrichment of Proteome-Scale Docking Profiles as a Biological Search-Space Reduction Layer for Protein Target Discovery . Identifying protein targets from phenotype-first or mechanism-uncertain compounds remains difficult because proteome-scale docking can generate thousands of structurally plausible interactions per compound. We developed a workflow that converts ranked proteome-scale docking profiles into stable Gene Ontology (GO) Biological Process enrichment signatures and evaluates whether those signatures can reduce the candidate target space while preferentially retaining known drug-target relationships. Docking targets were retained at the PDB-chain level, mapped to unique human gene identities, and analyzed with PANTHER overrepresentation against the screened structural gene universe. GO enrichment was evaluated from the top 25 through 505 ranked proteins in increments of 10, and a stable compound-level GO profile was selected using a Jaccard stability threshold of 0.80 across three consecutive transitions. Benchmarking used Yamanishi drug-target interactions, with 682 mapped compounds assigned to a prespecified development/held-out split