Beyond Structure Generation: Co-Folding Representations for Binding Affinity and ADMET Prediction

Binding affinity and enzyme-dependent metabolism are distinct molecular outcomes that both involve protein–ligand recognition. We introduce PairSense, a framework for predicting binding affinity and CYP substrate status from protein-conditioned co-folding representations without explicit structure generation. PairSense combines Boltz-2’s Pairformer embeddings with predicted pairwise distance distributions, retaining probabilistic geometric information while replacing distances derived from diffusion-generated coordinates. With the co-folding trunk frozen, we fine-tune only the affinity module on public data curated to remove benchmark overlap. PairSense improves over the original Boltz-2 by approximately 0.09 in Pearson correlation on the four-target FEP+ benchmark and 0.16 in Spearman correlation on OpenBind, with additional gains on CASP16 and OpenFE876. Keeping the affinity-adapted interaction representation fixed, we jointly fine-tune its native binary readout with molecular fingerprints on three cytochrome P450 substrate tasks. The joint model achieves higher mean scores than the published TDC leaderboard references. Controlled comparisons show that additional binding supervision improves CYP prediction and that protein context adds value beyond ligand features and task identity. These findings support co-folding representations as a reusable basis for predicting binding affinity and protein-mediated molecular properties beyond structure generation.