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bayesian-optimizer

Maintainer FreedomIntelligence · Last updated April 1, 2026

Bayesian optimization for experimental design and hyperparameter tuning in biomedical research.

OpenClawNanoClawAnalysisReproductionbayesian-optimizer🧠 bioos extended suitedrug discovery & designbayesian

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bayesian-optimizer

Skill Snapshot

2 min

The Bayesian Optimizer allows agents to efficiently explore a parameter space to maximize a target metric (yield, purity, binding affinity) with minimal experiments. It uses Gaussian Processes to model uncertainty and the Upper Confidence Bound (UCB) acquisition function.
When experiments are expensive or time-consuming.
To autonomously tune hyperparameters for a machine learning model.
To optimize reaction conditions (temperature, pH, concentration).
When experiments are expensive or time-consuming. To autonomously tune hyperparameters for a machine learning model. * To optimize reaction conditions (temperature, pH, concentration).

Source Doc

Next Step Proposal: Suggests the next best experiment parameters.
Surrogate Modeling: Predicts outcomes for untested parameters.
Exploration/Exploitation: Balances trying new things vs. refining known good results.