Benchmarks for Bio AI

Author

Bryan Tegomoh, MD, MPH

Published

May 24, 2026

Benchmarks are social infrastructure for scientific claims. A good benchmark narrows the space of plausible claims; it does not settle all uses of a model.

Learning Objectives
  • Match benchmarks to the biological decision under evaluation.
  • Use failure-aware metrics in molecular and cellular tasks.
  • Avoid benchmark overfitting and leakage.
TL;DR

Benchmarks matter when they are hard to game, close to the intended decision, and paired with failure analysis. A leaderboard is not a validation plan.

Introduction

CASP, CAMEO, MoleculeNet, and PoseBusters each illustrate a different benchmark role: blinded community assessment, continuous server evaluation, shared molecular datasets, and physical validity checks (Kryshtafovych et al., 2024; CAMEO, 2026; Wu et al., 2018; Buttenschoen et al., 2024).

Demonstrated

Demonstrated capability includes benchmark-driven progress in protein structure prediction and increasingly strict evaluation of molecular docking and generation. CASP15 documents categories beyond single-chain structure, including complexes, RNA, and ligand binding (Kryshtafovych et al., 2024). PoseBusters demonstrated that physically invalid poses can pass simpler docking metrics (Buttenschoen et al., 2024).

Evidence Anchor What It Supports Practical Constraint
CASP and CAMEO Structure prediction assessment Tasks evolve as methods improve
MoleculeNet Molecular property benchmark tasks Dataset splits shape conclusions
PoseBusters Physical validity in docking evaluation One metric can hide failure

Theoretical

Theoretical capability includes prospective discovery benchmarks where models choose experiments and are judged by cost-adjusted learning. This is the right direction for many life sciences tasks, but it is more expensive than static benchmark release.

Beyond Current Capabilities

Beyond current capabilities includes a universal biological benchmark that ranks all models. Biological tasks differ too much in ground truth, cost, and acceptable error.

Practice Notes

  • Use benchmarks to reject claims, not only to support them.
  • Prefer splits that reflect intended use.
  • Report failure categories beside average metrics.
  • Hold back prospective tests when the field is likely to overfit public leaderboards.