AssayBench

A benchmark for evaluating machine learning models on phenotypic screen prediction.

🌐 Website | Code | 🤗 Dataset | 📃 Paper |

0. News

We released a website with interactive data visualization!

1. Installation

pip install assaybench

Or install directly from the repository:

pip install git+ssh://git@github.com/Genentech/AssayBench.git

Or clone and install in editable mode:

git clone git@github.com:Genentech/AssayBench.git && cd AssayBench
pip install -e .

This installs the assaybench package, which provides:

• AssayBenchDataset — loads screens and splits from HuggingFace (Genentech/assaybench)
• RankingMetrics — computes ranking metrics (adjusted nDCG, precision, FDR, etc.)

With uv

You can add it to your project with

dependencies = [
    "assaybench @ git+ssh://git@github.com/Genentech/AssayBench.git",
]

2. Usage

Loading data and scoring a model

Each example in the dataset contains a question prompt describing a CRISPR screen, along with ground-truth relevance_genes and relevance_scores. To evaluate a model, pass its predicted gene ranking (a plain list[str]) together with the ground-truth genes and scores to RankingMetrics.evaluate():

from assaybench import AssayBenchDataset
from assaybench.benchmark.metrics import RankingMetrics

# Load the dataset with year-based splits
ds = AssayBenchDataset(
    dataset_name="biogrid",
    split_type="year",
    fold=0,
    novel_dataset_name="LaTest",
)
train, val, test, latest = ds.get_train_test_split()

# Define your model — any function that returns a ranked list of gene names
def my_model(prompt: str) -> list[str]:
    return ["BRCA1", "TP53", "MYC", ...]  # top predicted genes

# Score predictions
metrics = RankingMetrics(k_values=[10, 100])

for example in val:
    predicted_genes = my_model(example["question"])
    scores = metrics.evaluate(
        predicted_genes=predicted_genes,
        ground_truth_genes=example["relevance_genes"],
        relevance_scores=example["relevance_scores"],
    )
    print(f"Screen {example['dataset_name']}: AnDCG@100 = {scores['adjusted_ndcg@100']:.4f}")

See examples/load_data.ipynb for a complete walkthrough.

Dataset fields

Each screen returned by get_train_test_split() is a dictionary with the following fields:

Field	Type	Description
question	str	The prompt describing the screen and ranking task
relevance_genes	list[str]	All genes in the screen library
relevance_scores	list[float]	Thresholded percentile scores for each gene (higher = more relevant)
hit	list[bool]	Whether each gene is a hit in the screen
dataset_name	str	Screen identifier
screen_ids	list[int]	BioGRID screen ID(s) (>1 for merged duplicate screens)
phenotype	str	Full phenotype description
cleaned_phenotype	str	Coarse phenotype category (e.g. "Fitness / Proliferation / Viability")
condition_clause	str	Experimental condition (e.g. drug treatment, dose)
cell_type	str	Cell type used in the screen
cell_line	str	Cell line name
screen_type	str	Selection type (e.g. "Positive Selection", "Negative Selection")
library_methodology	str	Screen methodology (e.g. "Knockout", "Activation")
screen_rationale	str	Scientific rationale for the screen
screen_category	str	Screen directionality (e.g. "unidirectional", "bidirectional")
num_genes	int	Number of genes in the screen library
author	str	Publication author and year (e.g. "Wang T (2014)")
source_id	str	PubMed ID of the source publication
split	str	Data split assignment: train, validation, test, or novel_dataset
answer	str	Top 10 genes by relevance score (comma-separated, for reference)

Metrics

RankingMetrics.evaluate() returns a dictionary of scores. The primary metrics (computed at each k in k_values) are:

Metric	Description
ndcg@k	Normalized Discounted Cumulative Gain — measures ranking quality using graded relevance scores
adjusted_ndcg@k	nDCG adjusted for chance performance — the main benchmark metric (AnDCG)
precision@k	Fraction of top-k predictions that are hits
normalized_precision@k	Precision normalized by the number of true positives (NPrecision)
fdr@k	Fraction of top-k predictions that are non-hits (False Discovery Rate)
normalized_fdr@k	FDR normalized by the number of true negatives
recall@k	Fraction of true hits recovered in the top-k predictions
auroc	Area Under the ROC Curve over the full ranked list
mrr	Mean Reciprocal Rank — reciprocal of the rank of the first hit
hallucination_rate	Fraction of predicted genes not found in the screen library
hit_scaled_ndcg@k	nDCG computed using binary hit labels instead of graded relevance
hit_scaled_adjusted_ndcg@k	Adjusted nDCG using binary hit labels

By default all metric groups are computed. Pass metric_groups={"adjusted_ndcg", "precision"} to restrict to a subset.

Custom prompts

By default, AssayBenchDataset formats each screen's question field using a built-in prompt template (see src/assaybench/data/prompts/objective_prompts.yaml). You can override it by passing a prompt_template string to the constructor:

my_template = """
You are a genetics expert. Given the following CRISPR screen:
- Cell line: {cell_line} ({cell_type})
- Library: {library_type} ({library_methodology})
- Phenotype: {phenotype}

Rank the top 100 genes most likely to be hits.
Format: GENE1, GENE2, ..., GENE100
"""

ds = AssayBenchDataset(
    dataset_name="biogrid",
    split_type="year",
    fold=0,
    prompt_template=my_template,
)

The template is formatted with Python's str.format() using each screen's metadata fields. Available placeholders:

Placeholder	Description
{cell_line}	Cell line name
{cell_type}	Cell type description
{library_type}	Library type (e.g. "CRISPRn")
{library_methodology}	Methodology (e.g. "Knockout", "Activation")
{experimental_setup}	Experimental design (e.g. "Drug Exposure")
{duration}	Screen duration (e.g. "12 Days")
{condition_clause}	Condition details (e.g. " under Etoposide treatment (130.0 nM)")
{phenotype}	Phenotype description
{significance_criteria}	Statistical threshold for hit calling
{ranking_rationale}	What makes a gene rank highly
{notes}	Additional screen notes

Collecting LLM Results

Results from LLMs can be collected using this script; it uses DSPy and a couple additional instructions:

Your goal is to provide a list of genes that meet the screen criteria, even if you do not have access to the actual experimental data. The genes must use HGNC symbols. Use your knowledge of biology, gene function, and relevant pathways to predict which genes are most likely to be hits. Do not refuse to answer or say you need more data—make your best predictions based on your understanding of the biological context.

Example Command:

uv run python benchmarking/predictions_generation/collect_llm_predictions.py --config-name=collect-GLM-5

3. Paper reproduction

All figure scripts live in figures/ and read from a results cache built from the prediction files in benchmarking/predictions/.

Step 1: Build the results cache

cd figures
python generate_results_cache.py

This scores all prediction files against the ground truth and saves the results to figures/journal_figures_cache/results_cache.pkl.

To rescore only specific models (faster):

python generate_results_cache.py --model "gemini-3-pro" --model "gpt-5.4"

Step 2: Generate figures and tables

python plot0_proportions.py
python plot1_selected_methods.py
python plot2_phenotype_bar_plot_year.py
python plot3_duplicate_transfer_vs_model.py
python plot4_memorization_analysis.py
python plot5_scaling_laws.py
python plot6_bias.py

Outputs (PNG, PDF, LaTeX tables) are saved to figures/journal_figures/.

Script	Description
plot0_proportions.py	Dataset statistics table and phenotype composition pie charts
plot1_selected_methods.py	Main benchmark bar plot + LaTeX tables for selected methods
plot2_phenotype_bar_plot_year.py	Per-phenotype performance bar plot (year split)
plot3_duplicate_transfer_vs_model.py	Duplicate-screen cross-transfer vs model performance
plot4_memorization_analysis.py	Regression of performance on publication year, phenotype, and citations
plot5_scaling_laws.py	Qwen3.5 scaling laws (AnDCG@100 vs model size)
plot6_bias.py	Gene-level prediction bias analysis across models

Citation

If you found our work useful, please cite:

@misc{debrouwer2026assaybench,
      title={AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents}, 
      author={Edward De Brouwer and Carl Edwards and Alexander Wu and Jenna Collier and Graham Heimberg and Xiner Li and Meena Subramaniam and Ehsan Hajiramezanali and David Richmond and Jan-Christian Hütter and Sara Mostafavi and Gabriele Scalia},
      year={2026},
      eprint={2605.10876},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.10876}, 
}