Active learning

Skill Kdevos12/ALKYL/skills/active-learning

Claude Plugin for CompChem , Drug Discovery & Organic Chemistry reasoning

Install

npx -y skills add Kdevos12/ALKYL --skill active-learning

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when designing active learning or closed-loop molecular optimization (DMTA cycles). Covers query strategies (UCB/EI/BALD/QBC), batch DPP selection, docking oracles (Vina/Gnina), BEDROC/EF metrics, and Design-Make-Test-Analyze campaign management.

SKILL.md

3.1 KB, as published. Nobody here has run it

Active Learning for Drug Discovery

Purpose

Closed-loop molecular optimization: iteratively query the most informative compounds, label with assay/oracle, retrain model. Accelerates hit-to-lead and lead optimization by minimizing wet-lab experiments.

When to Use This Skill

Building a surrogate model to replace expensive docking/assay calls
Running a DMTA (Design-Make-Test-Analyze) loop
Accelerating virtual screening with AL-ranked acquisition
Combining QSAR uncertainty with experimental prioritization

Reference Files

Load specific references on demand:

File	Content
`references/al-theory.md`	Query strategies, acquisition functions, convergence, pool vs stream
`references/molecular-al.md`	Molecular representations, batch AL, diversity-reweighted sampling
`references/uncertainty-integration.md`	GP/conformal/ensemble signals → acquisition, calibration
`references/docking-al.md`	Surrogate docking oracle, VS acceleration, Logloss/BEDROC metrics
`references/dmta-loop.md`	Full DMTA cycle, stopping criteria, experiment prioritization, case studies

Quick Routing

"I want to find actives with fewest assay calls" → al-theory.md (query strategy) + molecular-al.md (batch AL)

"I want to accelerate a docking campaign" → docking-al.md (surrogate oracle)

"I have GP/conformal uncertainty, want to plug into AL loop" → uncertainty-integration.md

"I'm running a real DMTA cycle with a CRO" → dmta-loop.md

Core Loop Pattern

# Canonical active learning loop
labeled_pool = initial_dataset          # seed: 50–200 diverse cpds
unlabeled_pool = virtual_library        # 10k–1M candidates

for round in range(n_rounds):
    model.fit(labeled_pool.X, labeled_pool.y)
    scores = acquisition_fn(model, unlabeled_pool.X)  # uncertainty / EI / UCB
    batch = select_batch(unlabeled_pool, scores, k=batch_size)
    labels = oracle(batch)              # assay / docking / human expert
    labeled_pool = labeled_pool + (batch, labels)
    unlabeled_pool = unlabeled_pool - batch

Key Principles

Seed diversity matters — MaxMin or clustering on initial pool prevents early bias
Batch mode requires diversity — greedy top-k collapses; use DPP or greedy submodular
Calibrate before querying — miscalibrated uncertainty → wrong queries (use MAPIE/isotonic)
Track enrichment, not accuracy — AL goal is finding actives fast, not global R²
Stopping criterion — plateau in hit rate OR budget exhausted

Integration with ALKYL Skills

Uncertainty estimates: uncertainty-qsar skill (GP, conformal, deep ensembles)
Diversity selection: chem_diversity.py (MaxMin)
Docking oracle: docking skill (Vina/Gnina)
Library design: generative-design skill (REINVENT + AL reward)
Property filtering: chem_filter.py, chem_batch.py