We ran Pearl on OpenBind, a benchmark created to challenge co-folding and assess the field. It surpassed all other models — zero-shot.

Our Pearl system exceeded the other models on every metric, particularly on the most relevant measures that correspond to performance on real-world drug programs.

More detail for fellow nerds👇 https://www.genesis.ml/news/zero-shot-pearl-system-surpasses-all-cofolding-models-on-openbind

1/ Zero-shot means zero added help. Pearl got a protein sequence, a ligand SMILES, and an unbound template. Nothing else. (We also tested it providing a few binding-site residues, but zero-shot performance was already outstanding. → 78% on OpenBind's triple success criteria. Closest next best is 54%.

2/ 2Å is too fuzzy. The number that matters: sub-1Å. That's the real RMSD accuracy threshold that’s relevant to be useful in practice (for actually designing molecules and downstream predictions like potency). → Pearl: 60% → Best competitor: 27% → Most models: 1–13% At this bar, Pearl beats every method — including classical docking that gets the bound crystal structure Pearl never sees.

3/ This target is hard. A loop at the binding site shifts ~4Å when a ligand binds. The pocket doesn't even fit a ligand until the protein moves. Classical docking from the unbound structure can’t model this. This also isn’t a target with similar structures in the PDB, so co-folding models can’t cheat. Unlike some benchmarks, this makes the OpenBind challenge a proper test of real-world utility. Pearl predicts the motion from sequence alone and handles the new system with ease – including one compound placed to 0.28Å that no other zero-shot method solved.

4/ How we did it. Synthetic data at training. Equivariant architecture. Novel inference time scaling. AI + physics scoring to converge the outputs. This is what a foundation model that generalizes and works in actual drug discovery programs looks like — not one that memorizes the PDB.

Full write-up + figures 👇