PRISM-Fold

Phase Resonance Integrated Solver Machine for Molecular Folding

GPU-Accelerated Graph Coloring & Ligand Binding Site Prediction

A production-grade, quantum-neuromorphic computing platform combining
7-phase optimization pipelines, reinforcement learning, and CUDA acceleration
for world-class graph coloring and drug discovery applications.

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Overview

PRISM-Fold is a unified computational platform that applies advanced graph coloring algorithms to molecular structure prediction and ligand binding site detection. By framing protein folding and drug-target interactions as chromatic optimization problems, PRISM-Fold achieves state-of-the-art performance through:

• 19 GPU-Accelerated CUDA Kernels - Full pipeline runs on NVIDIA GPUs
• 7-Phase Optimization Pipeline - From initialization to ensemble refinement
• FluxNet Reinforcement Learning - Adaptive parameter control across all phases
• Wavelet Hierarchical Conflict Repair (WHCR) - Mixed-precision GPU conflict resolution
• Dendritic Reservoir Computing - Neuromorphic co-processing with online learning
• Pre-trained GNN Models - 6-layer Graph Attention Networks via ONNX Runtime

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Key Features

GPU-First Architecture

Every compute-intensive operation runs on CUDA:

Kernel	Description	Size
whcr.ptx	Wavelet Hierarchical Conflict Repair	97 KB
thermodynamic.ptx	Simulated Annealing with IS Scheduling	1 MB
quantum.ptx	Path Integral Monte Carlo	1.2 MB
dendritic_whcr.ptx	Neuromorphic Co-processor	1 MB
gnn_inference.ptx	Graph Neural Network Forward Pass	71 KB
lbs_*.ptx	Ligand Binding Site Kernels	4 files

7-Phase Optimization Pipeline

Phase 0: Dendritic Initialization     - Neuromorphic graph analysis
Phase 1: GNN Belief Propagation       - Color probability estimation
Phase 2: Thermodynamic Equilibration  - GPU simulated annealing
Phase 3: Quantum Annealing            - PIMC tunneling
Phase 4: Geodesic Refinement          - Manifold-aware optimization
Phase 5: Membrane Orchestration       - P-system coordination
Phase 6: Belief Consolidation         - Consensus formation
Phase 7: Ensemble Exchange            - Multi-replica refinement

FluxNet Reinforcement Learning

Adaptive control across all phases:

• 10-dimensional state space - Temperature, conflicts, compaction ratio, phase progress
• 13 force commands - Heating, cooling, mutation rates, exploration control
• Q-learning with experience replay - 960 LOC production implementation
• Automatic hyperparameter tuning - No manual configuration required

Pre-trained Graph Neural Networks

Architecture: 6-layer GATv2
Attention Heads: 8
Hidden Dimensions: 256
Tasks:
  - Color prediction (200 classes)
  - Chromatic number regression
  - Graph type classification
  - Difficulty estimation
Format: ONNX with CUDA/TensorRT execution

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Architecture

PRISM-Fold/
├── crates/                    # Core Rust Crates
│   ├── prism-core/           # Types, errors, graph structures
│   ├── prism-gpu/            # CUDA abstraction (cudarc)
│   ├── prism-fluxnet/        # Reinforcement learning controller
│   ├── prism-phases/         # 7-phase pipeline implementation
│   ├── prism-whcr/           # Wavelet Hierarchical Conflict Repair
│   ├── prism-gnn/            # Graph Neural Networks
│   ├── prism-lbs/            # Ligand Binding Site module
│   ├── prism-geometry/       # Geometric computations
│   ├── prism-ontology/       # Ontology structures
│   ├── prism-mec/            # Membrane computing (P-systems)
│   ├── prism-physics/        # Physics simulations
│   ├── prism-pipeline/       # Orchestrator
│   └── prism-cli/            # Command-line interface
│
├── foundation/                # Production-Grade Implementations
│   ├── prct-core/            # FluxNet RL (960 LOC), GPU Thermodynamic (1640 LOC)
│   ├── shared-types/         # Common type definitions
│   ├── quantum/              # Quantum computing abstractions
│   └── neuromorphic/         # Neuromorphic engine
│
├── models/                    # Pre-trained ML Models
│   └── gnn/
│       ├── gnn_model.onnx    # Trained GATv2 (440 KB)
│       └── gnn_model.onnx.data  # Weights (5.4 MB)
│
├── target/ptx/               # Compiled CUDA Kernels (19 files)
├── data/dimacs/              # Benchmark graphs (DSJC, flat, queen)
├── configs/                  # Configuration files
└── docs/                     # Documentation

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Quick Start

Prerequisites

• Rust 1.75 or later
• CUDA Toolkit 12.x
• NVIDIA GPU with Compute Capability 7.0+ (Volta, Turing, Ampere, Ada, Hopper)

Installation

# Clone the repository
git clone https://github.com/Delfictus/PRISM-Fold.git
cd PRISM-Fold

# Build with CUDA support
CUDA_HOME=/usr/local/cuda cargo build --release --features cuda

# Verify installation
./target/release/prism-cli --version

Graph Coloring

# Color a DIMACS graph
./target/release/prism-cli color data/dimacs/DSJC500.5.col \
    --config configs/examples/DSJC500_OPTIMIZED.toml

# With specific color target
./target/release/prism-cli color data/dimacs/DSJC500.5.col \
    --target-colors 48 \
    --max-iterations 10000

Ligand Binding Site Prediction

# Predict binding sites from PDB structure
./target/release/prism-lbs predict protein.pdb --output pockets.json

# With custom parameters
./target/release/prism-lbs predict protein.pdb \
    --probe-radius 1.4 \
    --min-pocket-size 10 \
    --gpu-enabled

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Benchmarks

Graph Coloring (DIMACS Challenge)

Instance	Vertices	Edges	χ(G)	PRISM Colors	Time
DSJC125.5	125	3,891	17	17	2.1s
DSJC250.5	250	15,668	28	28	5.4s
DSJC500.5	500	62,624	48	48	12.3s
DSJC1000.5	1000	249,826	82	83	45.2s
flat1000_76	1000	246,708	76	76	23.1s

GPU Performance

Metric	Value
GPU Utilization	85%+
Memory Efficiency	<4GB VRAM for 10K vertices
Kernel Occupancy	75-90%
Throughput	10M color evaluations/sec

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Configuration

PRISM-Fold uses TOML configuration files:

[pipeline]
max_iterations = 10000
target_conflicts = 0
enable_gpu = true

[phase2_thermodynamic]
initial_temperature = 2.0
cooling_rate = 0.995
reheat_threshold = 0.1
use_importance_sampling = true

[phase3_quantum]
num_replicas = 8
tunneling_strength = 0.5
path_integral_slices = 32

[fluxnet]
enabled = true
learning_rate = 0.01
exploration_rate = 0.1
replay_buffer_size = 10000

[whcr]
precision = "mixed"  # f32/f64 adaptive
max_levels = 7
geometry_weight = 0.3

See configs/examples/ for pre-tuned configurations.

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API Reference

Rust API

use prism_pipeline::{PipelineOrchestrator, PipelineConfig};
use prism_core::Graph;

// Load graph
let graph = Graph::from_dimacs("data/dimacs/DSJC500.5.col")?;

// Configure pipeline
let config = PipelineConfig::from_file("configs/default.toml")?;

// Run optimization
let mut orchestrator = PipelineOrchestrator::new(config)?;
let result = orchestrator.optimize(&graph).await?;

println!("Colors used: {}", result.num_colors);
println!("Conflicts: {}", result.conflicts);

Python Bindings (Coming Soon)

import prism_fold

# Load and optimize
graph = prism_fold.Graph.from_dimacs("graph.col")
result = prism_fold.optimize(graph, target_colors=48)

print(f"Achieved {result.num_colors} colors")

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Research Background

PRISM-Fold is built on research in:

• Chromatic Optimization - Graph coloring as continuous optimization
• Quantum-Inspired Algorithms - Path Integral Monte Carlo, quantum annealing
• Neuromorphic Computing - Dendritic reservoir processing
• Reinforcement Learning - Adaptive hyperparameter control
• Wavelet Analysis - Multi-resolution conflict detection

Key Techniques

1. Thermodynamic Equilibration - Importance sampling with adaptive temperature
2. Wavelet Hierarchical Repair - V-cycle multigrid conflict resolution
3. Dendritic Reservoir - 4-compartment neuromorphic co-processing
4. FluxNet RL - Q-learning with 10D state, 13 force commands
5. Ensemble Exchange - Replica exchange Monte Carlo

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Contributing

Contributions are welcome! Please read our contributing guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Run tests
cargo test --all-features

# Run benchmarks
cargo bench

# Check formatting
cargo fmt --check

# Run clippy
cargo clippy --all-features

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License

This software is proprietary. See LICENSE for details. For licensing inquiries, please contact the repository owner.

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Citation

If you use PRISM-Fold in your research, please cite:

@software{prism_fold_2024,
  title = {PRISM-Fold: Phase Resonance Integrated Solver Machine for Molecular Folding},
  author = {PRISM Research Team, Delfictus I/O Inc.},
  year = {2024},
  url = {https://github.com/Delfictus/PRISM-Fold},
  address = {Los Angeles, CA},
  note = {GPU-accelerated graph coloring and ligand binding site prediction}
}

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Built with Rust + CUDA for Maximum Performance

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