Protein models excel at retrieving data but struggle with geometry tasks
ProteinGeoBench: Frontier Structural-Biology Agents Are Fluent at Lookup but Lost in Geometry
Artificial IntelligenceMachine Learning
Summary
This paper looks at how well advanced computer models understand protein structures. The authors find that these models are good at identifying and retrieving information about proteins but are less accurate when asked to calculate geometric details. The evaluation also shows that scores depend a lot on how tests are set up and which tasks are included. The authors recommend reporting results by task type and being clear about testing conditions.
What this means in practice
- •For structural biology tool developers: Design evaluation methods that separately assess data retrieval and geometry computation capabilities in protein structure models.
- •For computational chemistry software teams: Use task-specific performance reporting to improve accuracy in protein modeling software for drug design and molecular analysis.
Authors
Matt Turk, Brandon L. Neel, Diana M. Lopez, Zachary D. Smith, Grace N. Johnecheck, James B. Golden
Abstract
Frontier agents retrieve structural metadata well and compute geometry from coordinates less reliably. We ask how much of a benchmark score reflects that gap and how much reflects the evaluation configuration around the model. We study ProteinGeoBench, a suite of 131 protein-structure tasks, using 4,249 rollouts from four frontier models and 4,486 further trials across 26 models. Three of the four frontier models fall within 0.031 of one another (0.785 to 0.816) with no difference between them resolved, while the fourth scores 0.707 and all three gaps to it exclude zero. Three of the four do better on metadata lookup than on numeric calculation. The fourth is slightly better on numeric. Numeric tasks are 28 of the 131 but half of the twelve tasks that all four models fail. The ranking also depends on the evaluation budget: with one run per task, 23.9% of bootstrap replicates put a different model first. On a selected 15-task slice, removing network access lowers scores more than blocking eight structural-database domains, but the two contrasts were graded in different passes and cannot be cleanly decomposed. Swapping in each model's own command-line agent changes scores by -0.071 to +0.086, a comparison too small to rule out moderate harness effects. Across 26 models, numeric performance rises with non-numeric performance rather than staying flat. The experiments use different task subsets, so they cannot rank model, harness, access and task composition against one another. Evaluations should report results by task family, along with the number of runs, the harness and the access conditions.