mcmcscreen 0.1.0

Uncertainty-aware drug candidate screening via Bayesian MCMC with permanent Arweave archiving

MCMCScreen

Uncertainty-aware drug candidate screening via Bayesian MCMC with permanent Arweave archiving.

MCMCScreen applies Bayesian Markov Chain Monte Carlo (MCMC) inference to dose-response data, quantifies prediction uncertainty for each drug candidate, and permanently archives screening results on the Arweave decentralized network.

Why MCMCScreen?

AI-driven drug discovery pipelines generate thousands of candidate molecules — but most tools report point estimates without uncertainty. MCMCScreen addresses this gap by:

• Fitting a Bayesian Hill equation model to dose-response data via MCMC
• Ranking candidates by posterior IC50 estimates
• Flagging unreliable predictions (high uncertainty CV)
• Archiving all results permanently on Arweave (immutable, verifiable)

Installation

pip install mcmcscreen

Quick Start

from mcmcscreen import MCMCScreener, ArweaveUploader

# Define candidates
candidates = [
    {
        "id": "compound_A",
        "concentrations": [0.1, 0.3, 1.0, 3.0, 10.0, 30.0],
        "responses": [2, 8, 25, 55, 80, 92],
    },
    {
        "id": "compound_B",
        "concentrations": [0.1, 0.3, 1.0, 3.0, 10.0, 30.0],
        "responses": [1, 3, 10, 30, 60, 85],
    },
]

# Screen and rank
screener = MCMCScreener(n_samples=1000, n_chains=2, random_seed=42)
ranked = screener.screen(candidates)

for r in ranked:
    print(f"{r['compound_id']}: IC50={r['ic50_mean']:.2f} uM, reliable={r['reliable']}")

# Archive to Arweave
screener.fit(
    candidates[0]["concentrations"],
    candidates[0]["responses"],
    compound_id="compound_A"
)
manifest = screener.to_manifest()
uploader = ArweaveUploader()  # mock mode without wallet
result = uploader.upload(manifest)
print(result)

Features

• Bayesian Hill equation dose-response modeling (PyMC 5)
• Posterior IC50 estimation with HDI (highest density interval)
• Uncertainty coefficient of variation (CV) flagging
• Multi-candidate ranking
• Arweave permanent archiving (mock mode available)
• Fully reproducible (fixed random seed)

Citation

If you use MCMCScreen, please cite:

Kim, D. (2026). MCMCScreen: Uncertainty-aware drug candidate screening via Bayesian MCMC with permanent Arweave archiving. Promptgenix LLC.

License

MIT License. Copyright (c) 2026 Dohoon Kim, Promptgenix LLC.