Code and Data for "Reproducible Adaptive MCMC via Sharing a Pretrained Generator Matrix Across Runs and Structures"

This repository contains all code, data, and figure-generation scripts for reproducing the results in: "Reproducible Adaptive MCMC via Sharing a Pretrained Generator Matrix Across Runs and Structures"Masato Tanigawa and Takafumi IwakiJournal of Chemical Information and Modeling, 2026 KEY FINDINGS - Shared-M protocol reduces cross-run variability by 68%- Two failure modes identified: Freezing (25K steps, Acc < 5%) and Over-adaptation (100K steps, variance doubles)- Optimal training length: ~50K steps (healthy acceptance 10-30%, lowest cross-seed variance)- Transferability: Pretrained M transfers across structures (gap < 1 Å)- Critical insight: Minimizing apparent reproducibility metrics can select for over-adaptation CONTENTS - scripts/: Core GM-MCMC implementation and all experiment scripts (Exp1-Exp5)- data/: Raw results (CSV), including individual seed-level data- figures/: Publication-quality figure generation scripts (matplotlib)- structures/: PDB files (1BNA, 1NAJ) used as test systems REPRODUCTION pip install numpy matplotlibpython scripts/run_all_experiments_fast.py # ~5 minpython figures/plot_all_figures.py LICENSE MIT License