Beyond the Ligand Applicability Domain: Generalization and Local Resolution in Structure-Aware Foundation Models for Affinity Prediction
Year: 2026
Type: preprint
Abstract: Structure-aware foundation models that jointly represent proteins and ligands promise to extend affinity prediction beyond the chemical applicability domains of conventional ligand-based models, but whether they provide sufficient resolution to guide fine-grained medicinal-chemistry decisions remains unclear. We evaluatedBoltz-2across12kinasetargetsusinghistoricalbioactivityreconstruction,binding-pocket randomization, cross-target selectivity analysis, and chemical-series evaluation. Across chemically diverse compounds in temporally reconstructed evaluation sets, Boltz-2 and target-specific ligand-based machine learningshowedcomparablemeanglobalrankingacrosstargets,withsubstantialtarget-dependent differences (mean Kendall’s τ = 0.40 vs 0.31). Randomizing the binding pocket reduced mean τ from 0.33 to 0.14, while predictions were centered near −7.64 kcal/mol and retained correlated compound profiles acrossproteins.Together,theseresultssuggestastrongligand-conditionedsignal,withadditional predictive information contributed by the protein pocket. Despite this protein sensitivity, compound-level selectivitypredictionremainedweak(meanτ=0.17),andtheexperimentallystrongesttargetwas recovered for only 18.9% of compounds. Ranking also weakened within 72 temporally classified chemical series and varied substantially among series (protein-balanced mean τ = 0.18 for Boltz-2 and 0.09 for ligand-based models). These results show that structure-aware models can recover affinity information beyond relationshipscaptured by conventionalligand-based approaches whilerevealingaresolution boundary between broad prioritization and the local energetic discrimination required for selectivity and analogue ranking. At their current demonstrated resolution, such models appear best suited to broad compound or series prioritization and complementary hypothesis generation. Extending their use to fine analogueranking,selectivityoptimization,andinterpretationofsmallaffinitydifferenceswillrequire prospective evidence of reliable local resolution.
Source: ChemRxiv
Institutions: Medical University of South Carolina, University of Pretoria
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7220863842