Evaluation Principles for Biomedical Discovery AI

Evaluation in life sciences AI must respect biology, not only machine learning convention. The wrong split, metric, or benchmark often makes a model look useful before it meets a new assay or a new laboratory.

Learning Objectives

• Choose evaluation splits that match biological use.
• Match metrics to experimental decisions.
• Detect leakage, scaffold bias, homolog leakage, and assay leakage.

TL;DR

The core evaluation question is not whether a model performs well on held-out rows. The core question is whether it improves a real experimental decision under the distribution where it will be used.

Introduction

Benchmarks in structural biology, molecular docking, and single-cell perturbation illustrate the same principle from different angles. CASP evaluates protein structure methods against blinded targets (Kryshtafovych et al., 2024). CAMEO provides continuous automated structure-prediction evaluation (CAMEO, 2026). PoseBusters showed that RMSD alone misses physically implausible docking outputs (Buttenschoen et al., 2024).

Demonstrated

Demonstrated capability includes evaluation regimes that expose failure modes hidden by simple metrics. CASP and CAMEO support community-level structure prediction assessment (Kryshtafovych et al., 2024; CAMEO, 2026). PoseBusters demonstrated that docking outputs need chemical and physical validity checks in addition to geometric error (Buttenschoen et al., 2024).

Evidence Anchor	What It Supports	Practical Constraint
CASP	Blinded community assessment for structure prediction	Targets and categories change across rounds
CAMEO	Continuous server evaluation	Automated evaluation depends on target release and criteria
PoseBusters	Physical plausibility checks for docking poses	RMSD-only evaluation rewards incomplete success

Theoretical

Theoretical capability includes benchmark suites that forecast real discovery productivity. Such benchmarks are plausible when they contain prospective experiments, cost-aware decisions, and multiple failure categories. They remain incomplete when they only compare model scores.

Beyond Current Capabilities

Beyond current capabilities includes a universal score that ranks models across every biological domain. Structure prediction, compound screening, cellular response, and clinical translation require different ground truth and different error costs.

Practice Notes

• Use scaffold, sequence-family, cell-line, target, and time splits when those match deployment.
• Report calibration, uncertainty, and failure modes beside headline metrics.
• Include physical, chemical, and biological validity checks.
• Prefer prospective validation when model output drives experiment selection.