BioAssayAlign-Qwen3-Embedding-0.6B-Compatibility

BioAssayAlign Qwen3-Embedding-0.6B Compatibility

<p align="center"> <img src="./bioassayalign.png" alt="BioAssayAlign logo" width="280"> </p>

What this model is

BioAssayAlign is an assay-conditioned small-molecule ranking model.

It takes:

• one assay definition
• a submitted list of candidate SMILES

and returns:

• one compatibility score per candidate
• a ranked shortlist for that assay

This model is designed to answer a practical question:

Given this assay, which molecules in my current candidate list should I screen first?

It is not:

• a chatbot
• a generative chemistry model
• a direct potency regressor
• a calibrated probability model

Companion dataset

Public dataset:

• BioAssayAlign Assay-Compound Data

The published model was trained on the prepared compatibility-ranking subset inside that dataset release.

Intended use

Use this model when you already have a candidate set and want a ranking signal for one assay at a time.

Reasonable uses:

• shortlist triage before wet-lab screening
• retrospective ranking experiments
• assay-conditioned ranking features in a downstream workflow

Not reasonable uses:

• reading the raw score as a probability of success
• predicting exact IC50 / EC50 / Ki values
• comparing raw scores across unrelated runs as if they were globally calibrated

How to run it locally

This repository is self-contained for inference. You do not need the original training codebase to run the published model.

Install

python -m pip install -r requirements.txt

Minimal local example

from bioassayalign_compatibility import (
    AssayQuery,
    load_compatibility_model_from_hub,
    rank_compounds,
    serialize_assay_query,
)

model = load_compatibility_model_from_hub(
    "lighteternal/BioAssayAlign-Qwen3-Embedding-0.6B-Compatibility"
)

assay_text = serialize_assay_query(
    AssayQuery(
        title="JAK2 inhibition assay",
        description="Cell-based luminescence assay measuring JAK2 inhibition in HEK293 cells.",
        organism="Homo sapiens",
        readout="luminescence",
        assay_format="cell-based",
        assay_type="inhibition",
        target_uniprot=["O60674"],
    )
)

results = rank_compounds(
    model,
    assay_text=assay_text,
    smiles_list=[
        "CC(=O)Nc1ncc(C#N)c(Nc2ccc(F)c(Cl)c2)n1",
        "c1ccccc1",
        "CCO",
    ],
)

for row in results:
    print(row)

What to provide

Best practice:

• provide structured assay fields rather than one free-form paragraph
• include target, readout, organism, and format when known
• submit one parent or cleaned SMILES per candidate

Recommended assay fields:

• title
• description
• organism
• readout
• assay_format
• assay_type
• target_uniprot

The model is reasonably robust to wording changes, but missing metadata can reduce ranking quality.

Model details

Published artifact configuration:

Important:

• the published score is not a raw embedding dot product
• the ranking comes from the learned scorer head

Training data

The public artifact was trained on a frozen assay-compound corpus derived from:

• PubChem BioAssay
• ChEMBL

The published model uses the prepared compatibility-ranking subset from:

• lighteternal/BioAssayAlign-Assay-Compound-Data

Prepared training dataset

Preparation rules

Each training group contains:

• one assay
• one positive compound
• multiple explicit same-assay inactive compounds

This is a ranking setup, not a generic text-retrieval setup.

Training configuration

Results

Main evaluation

Interpretation:

• the model materially beats the random ranking baseline
• it is strongest as a within-list ranking tool
• the main output to trust is the ranking order and shortlist separation, not the raw score magnitude

Score interpretation

The raw output is a learned compatibility logit-like score.

What it means:

• higher is better
• differences are meaningful within the same submitted list
• absolute values are not calibrated across unrelated runs

Example:

• candidate A score: 6.25
• candidate B score: -8.65
• candidate C score: -23.37

This does not mean A has a literal probability or potency attached to it. It means A ranked substantially above B and C for that submitted assay and candidate set.

For user-facing interpretation, the recommended order is:

1. rank
2. relative shortlist score within the submitted list
3. chemistry context columns
4. raw model score only for debugging or export

If you want a normalized within-list view, you can compute:

• min-max scaling to 0–100
• or softmax over the submitted list

Those are still not calibrated biological probabilities.

Example predictions

These examples were produced from the published weights.

Example: JAK2 cell assay

Assay:

• title: JAK2 inhibition assay
• description: Cell-based luminescence assay measuring JAK2 inhibition in HEK293 cells.
• organism: Homo sapiens
• readout: luminescence
• assay format: cell-based
• assay type: inhibition
• target UniProt: O60674

Example: ALDH1A1 fluorescence assay

Assay:

• title: ALDH1A1 inhibition assay
• description: Cell-based fluorescence assay measuring ALDH1A1 inhibition in human cells.
• organism: Homo sapiens
• readout: fluorescence
• assay format: cell-based
• assay type: inhibition
• target UniProt: P00352

Limitations

• The score is not a calibrated probability.
• The model does not predict exact potency values.
• The benchmark is assay-held-out, not a universal unseen-scaffold benchmark.
• Public assay data is noisy and assay protocols are heterogeneous.
• Some assays remain difficult and yield only moderate separation.
• Use the model as a ranking aid, not as a stand-alone medicinal chemistry decision system.

Repository contents

Files provided in this HF model repo:

• best_model.pt
• training_metadata.json
• training_summary.json
• bioassayalign_compatibility.py
• requirements.txt

Interactive Space

Use the model in the companion Space:

• https://huggingface.co/spaces/lighteternal/BioAssayAlign-Compatibility-Explorer