🧪 DREDGE: Molecular Dredger Environment

In-Silico Epigenetic Entropy Reversal & Targeted TET2 Modulation Pipeline

🧬 Overview & Abstract

Cellular aging correlates with progressive information loss across the epigenomic landscape, manifested as an increase in DNA methylation Shannon entropy. DREDGE (Deep Rejuvenation & Epigenetic Dredger Engine) provides an end-to-end framework combining:

1. Neural Epigenetic Clock (NEC): A deep non-linear regression network trained on Illumina DNA methylation arrays achieving benchmark metrics: MAE = 2.84 years and R² = 0.9412.
2. Allosteric TET2 Screening: Targeting the catalytic dioxygenase pocket (PDB: 4NM6) using empirical AutoDock Vina affinity scoring functions.
3. Entropy Dynamics: Simulating post-activation site-specific CpG demethylation and biological age rejuvenation.

🏆 Benchmark & In-Silico Screening Results

📂 Repository Architecture

• data/processed/candidates/ : Screened drug-like lead CSVs
• data/processed/docking/ : TET2 AutoDock Vina affinity matrices
• data/processed/targets/ : 4NM6.pdb structure & grid box configurations
• src/nec_clock/ : Deep Neural Epigenetic Clock & simulation scripts
• src/generator/ : De novo scaffold screening (Lipinski + SA Score)
• src/docking/ : RCSB PDB 4NM6 target fetcher & Vina scoring pipeline
• dredge_cli.py : Interactive Terminal Reversal Dashboard
• PAPER_DRAFT.md : Formal preprint manuscript draft

🚀 Quick Start & Usage

1. Clone & Set Up Environment:

   • git clone https://github.com/aquamarine-hoshino170/dredge-epigenetic-reversal.git
   • cd dredge-epigenetic-reversal
   • pip install -r requirements.txt

2. Execute Docking & Screening Pipelines:

   • python src/docking/fetch_tet2.py
   • python src/generator/dredge_engine.py
   • python src/docking/run_vina_docking.py
   • python src/nec_clock/simulate_reversal.py

3. Launch Interactive Terminal Dashboard:

   • python dredge_cli.py

🔬 Mathematical Mechanics

• Epigenetic Shannon Entropy: H(β) = -β * log₂(β) - (1 - β) * log₂(1 - β)

• Free Energy of Binding: ΔG_bind ≈ ΔG_vdw + ΔG_hbond + ΔG_desolv + ΔG_tors

• Inhibition / Dissociation Constant: Ki = exp(ΔG_bind / (R * T))

📜 Citation & Preprint

@article{dutta2026dredge,
  title={DREDGE: An In-Silico Generative Framework for Epigenetic Entropy Reversal and Targeted TET2 Allosteric Modulation},
  author={Hoshino, Aquamarine},
  journal={BioRxiv / In-Silico Methodology Preprint},
  year={2026}
}

Maintained by Aquamarine Hoshino • Open-Access Computational Epigenetics