research
The Binder Model Wrote the Shortlist Rule, Not the Protein
Language models combined precomputed structural proxy scores to rank existing protein-binder candidates.
Summary
Language models combined precomputed structural proxy scores to rank existing protein-binder candidates.
On a ten-target held-out split, five sampled global policies reached 0.589 Recall@10 versus 0.571 for the strongest single-feature baseline. The authors frame the method as an interpretable post-generation decision layer for scarce wet-lab slots, not a new binder generator or evidence of biological efficacy.
Why it matters
Language models combined precomputed structural proxy scores to rank existing protein-binder candidates.
Limits and context
- The authors frame the method as an interpretable post-generation decision layer for scarce wet-lab slots, not a new binder generator or evidence of biological efficacy.
Key claims
Language models combined precomputed structural proxy scores to rank existing protein-binder candidates.
Qualification: The authors frame the method as an interpretable post-generation decision layer for scarce wet-lab slots, not a new binder generator or evidence of biological efficacy.
Evidence: source-2026-08-24-013
Sources
- arXiv preprint 2608.20755arXiv · primary research
Corrections
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