CB7Mol: A Physicochemically Grounded AI-driven Model for CB[7] Affinity Prediction

AI-driven scientific discovery requires more than predictive accuracy: models must remain reliable under scarce supervision while respecting underlying physicochemical principles. We study this challenge through cucurbit[7]uril (CB[7])-based host–guest binding affinity prediction, an important problem in molecular recognition for supramolecular chemistry. A fundamental challenge is to jointly acquire the molecular, geometric, and affinity knowledge required for grounded prediction despite limited CB[7]-specific data. We propose CB7Mol, a physicochemically grounded AI-driven model that strategically transfers complementary knowledge from compatible sources to bridge these three gaps: molecular encoder post-training acquires CB[7]-relevant chemical semantics while preserving geometry; host–guest complex pre-training learns binding geometry and conformational flexibility from resolved crystal structures; and cross-host calibration transfers affinity supervision from mechanistically compatible macrocyclic hosts. CB7Mol achieves the best overall average ranking across three CB[7] affinity evaluation sets. More importantly, mechanistic analyses show that its predictions reflect key physicochemical determinants of CB[7] recognition rather than dataset-specific correlations, supporting reliable discovery beyond known guest chemistries. Finally, an AI-to-experiment pipeline screens 304k molecules in GEOM-Drugs and prioritizes candidates for wet-lab validation. CB7Mol recovers established strong-binding scaffolds, uncovers physicochemically consistent candidates with novel scaffolds, and yields high-affinity guests validated by competition NMR with $\log K_a$ up to approximately 12.6, expanding the experimentally validated CB[7] chemical space and molecular toolbox for supramolecular design.