omtx 2.0.2

Python SDK for Om

OMTX Python SDK

Minimal Python client for the OMTX Gateway.

Core scope:

• Submit diligence jobs
• Poll job status/results
• Request subscription-gated shard URLs
• Load entitlement-scoped data into an OmDataFrame (Polars-backed)

Installation

Core SDK:

pip install omtx

Quick Start

from omtx import OmClient

with OmClient() as client:
    print(client.status())

    job = client.diligence.deep_diligence(
        query="CRISPR applications in cancer therapy",
        preset="quick",
    )

    result = client.jobs.wait(
        job["job_id"],
        result_endpoint="/v2/jobs/deep-diligence/{job_id}",
    )
    print(result.get("result", {}).get("total_claims"))

Setup

export OMTX_API_KEY="your-api-key"

The SDK targets https://api.omtx.ai.

For develop/staging testing, override the base URL explicitly:

gcloud run services describe api-gateway \
  --region us-central1 \
  --project omtx-diligence \
  --format=json | jq -r '.status.traffic[]? | select(.tag=="develop") | .url'

Then use that URL in the client:

from omtx import OmClient

with OmClient(
    api_key="your-api-key",
    base_url="https://develop---api-gateway-...a.run.app",
) as client:
    print(client.status())

Or with env:

export OMTX_BASE_URL="https://develop---api-gateway-...a.run.app"

When a non-production host is used, the SDK emits a warning.

Data Access

Primary training flow (separate pools):

binders = client.load_binders(
    protein_uuid="550e8400-e29b-41d4-a716-446655440000",
    n=1000,          # optional: random sample size
    sample_seed=42,  # optional: deterministic sampling
)

nonbinders = client.load_nonbinders(
    protein_uuid="550e8400-e29b-41d4-a716-446655440000",
    n=10000,         # optional: random sample size
    sample_seed=42,  # optional: deterministic sampling
)

print(binders.shape, nonbinders.shape)
binders.show(top_n=24, sort_by="binding_score")
binders.show(top_n=24, sort_by="selectivity_score")
# show() renders inline in notebooks; no extra display() wrapper needed.

Manual shard export URLs (advanced use):

urls = client.binders.urls(
    protein_uuid="550e8400-e29b-41d4-a716-446655440000",
)
print("Binder shard URLs:", len(urls["binder_urls"]))
print("Non-binder shard URLs:", len(urls["non_binder_urls"]))
print("First binder URL:", urls["binder_urls"][0] if urls["binder_urls"] else None)

Generated proteins available now:

protein_uuids = client.datasets.generated_protein_uuids()
print("Generated protein UUIDs:", protein_uuids[:5])

Module-level convenience:

import omtx as om

binders = om.load_binders(
    protein_uuid="550e8400-e29b-41d4-a716-446655440000",
    n=1000,
    sample_seed=42,
)
nonbinders = om.load_nonbinders(
    protein_uuid="550e8400-e29b-41d4-a716-446655440000",
    n=10000,
    sample_seed=42,
)
print(binders.shape, nonbinders.shape)

Chemprop Training (Binary Binder Classification)

Use load_binders(...) and load_nonbinders(...) to build a labeled dataset (is_binder=1/0) and train Chemprop in classification mode.

Prepare training CSV:

import polars as pl
from omtx import OmClient

PROTEIN_UUID = "550e8400-e29b-41d4-a716-446655440000"

with OmClient() as client:
    binders_df = (
        client.load_binders(
            protein_uuid=PROTEIN_UUID,
            n=50000,
            sample_seed=42,
        )
        .to_polars()
        .with_columns(pl.lit(1).alias("is_binder"))
    )
    non_binders_df = (
        client.load_nonbinders(
            protein_uuid=PROTEIN_UUID,
            n=200000,
            sample_seed=42,
        )
        .to_polars()
        .with_columns(pl.lit(0).alias("is_binder"))
    )

train_df = (
    pl.concat([binders_df, non_binders_df], how="vertical_relaxed")
    .select(["smiles", "is_binder"])
    .drop_nulls()
    .unique()
    .sample(fraction=1.0, shuffle=True)
)

train_df.write_csv("chemprop_train.csv")
print(train_df.shape)

Equivalent script:

python examples/prepare_chemprop_binary.py \
  --protein-uuid 550e8400-e29b-41d4-a716-446655440000 \
  --binders 50000 \
  --non-binders 200000 \
  --output chemprop_train.csv

Train:

chemprop train \
  --data-path chemprop_train.csv \
  --task-type classification \
  --smiles-columns smiles \
  --target-columns is_binder \
  --split-type scaffold_balanced \
  --split-sizes 0.8 0.1 0.1 \
  --epochs 50 \
  --batch-size 64 \
  --output-dir chemprop_runs/binder_cls

Predict:

chemprop predict \
  --test-path infer.csv \
  --model-paths chemprop_runs/binder_cls \
  --smiles-columns smiles \
  --preds-path infer_preds.csv

Idempotency

• Every non-GET call gets an idempotency key automatically.
• All diligence POST helpers accept idempotency_key=... for explicit retry control.

Helper Surface

• diligence.deep_diligence(query, preset=None, idempotency_key=None, **kwargs)
• diligence.synthesize_report(gene_key, idempotency_key=None)
• diligence.search(query, idempotency_key=None)
• diligence.gather(query, idempotency_key=None)
• diligence.crawl(url, max_pages=5, idempotency_key=None)
• diligence.list_gene_keys()
• jobs.history(...), jobs.status(job_id), jobs.wait(job_id, ...)
• binders.get_shards(...)
• binders.urls(...)
• load_binders(...)
• load_nonbinders(...)
• datasets.catalog()
• datasets.generated_protein_uuids()
• status()
• users.profile()

Route policy:

• /v2/diligence/getTargetDiligenceReport remains an alias route and is not a separate SDK helper.
• /v2/rag/search is intentionally not exposed in the SDK.

Migration

Breaking changes in 2.0.0:

• OMTXClient removed.
• OmClient is now the only supported client class.
• Legacy pricing helpers removed from SDK surface.
• Legacy binder batch-cost helper removed from SDK surface.
• Shard access now resolves latest accessible dataset by protein_uuid.
• client.status() is the primary health helper.
• load_binders(...) and load_nonbinders(...) are the primary dataframe-loading helpers.
• Flat shard URL aliases are available as binder_urls / non_binder_urls.
• Core SDK runtime includes polars + rdkit.

Migration mapping (1.x -> 2.x):

• from omtx import OMTXClient -> from omtx import OmClient
• OMTXClient(...) -> OmClient(...)

Breaking changes in 1.0.0:

• binders.get(...) removed from core SDK.
• binders.iter(...) removed from core SDK.
• pandas removed from required dependencies.

Migration mapping (0.x -> 1.x):

• binders.get(...) -> client.load_binders(...) / client.load_nonbinders(...) or binders.get_shards(...)
• binders.iter(...) -> binders.get_shards(...) + application-level streaming
• pip install omtx (with pandas) -> pip install omtx (with polars + rdkit)

Full details: see MIGRATION.md.

Requirements

• Python >=3.9
• OMTX API key

License

MIT. See LICENSE.