# Pairwise relative learning

Enzyme kinetics prediction has relied on absolute regression over heterogeneous data, where the same enzyme-substrate system can differ by orders of magnitude across studies and models drift toward the mean. DeltaKcat recasts the task as pairwise relative learning: within one study it fixes either the enzyme or the substrate and a siamese network predicts the kinetic difference between two enzyme-substrate complexes, after which an experimentally characterized reference complex restores absolute values. Under a cold-start split, absolute kcat prediction reaches R² 0.71 and PCC 0.85, against R² 0.14 to 0.48 for DLKcat, CataPro and UniKP; on external adenylate kinase data excluded from training, zero-shot PCC is 0.40.

Enzyme kinetics prediction has relied on absolute regression over heterogeneous data, where the same enzyme-substrate system can differ by orders of magnitude across studies and models drift toward the mean. DeltaKcat recasts the task as pairwise relative learning: within one study it fixes either the enzyme or the substrate and a siamese network predicts the kinetic difference between two enzyme-substrate complexes, after which an experimentally characterized reference complex restores absolute values. Under a cold-start split, absolute kcat prediction reaches R² 0.71 and PCC 0.85, against R² 0.14 to 0.48 for DLKcat, CataPro and UniKP; on external adenylate kinase data excluded from training, zero-shot PCC is 0.40.