rxnrecer 1.3.4

Deep learning framework for predicting enzyme-catalyzed reactions from protein sequences

RXNRECer

RXNRECer v1.3.4 is a deep learning framework for predicting enzyme-catalyzed reactions from protein sequences. It is the official implementation of "RXNRECer: Active Learning with Protein Language Models for Fine-Grained Enzyme Reaction Prediction."

🎉 Now available on PyPI for easy installation!

🚀 Features

• Multi-Stage Prediction: S1 (reaction prediction), S2 (reaction integration), S3 (LLM reasoning)
• Protein Sequence Analysis: Process protein sequences in FASTA format
• Deep Learning Models: ESM-2 embeddings with advanced neural architectures
• GPU Acceleration: CUDA support for faster inference
• Easy-to-use CLI: Simple command-line interface with comprehensive options
• Smart Caching: Automatic result caching for faster repeated predictions

📋 Requirements

• Python 3.10+
• PyTorch 2.0+
• CUDA 11.0+ (recommended)
• 32GB+ RAM
• 40GB+ disk space

🚀 Quick Start

1. Install (Recommended)

# Install from PyPI (recommended)
pip install rxnrecer

# Or install from GitHub
pip install git+https://github.com/kingstdio/RXNRECer.git

2. Download Data

# Download required data and model files (~35.8GB total)
rxnrecer-download-data

# Or download separately
rxnrecer-download-data --data-only      # ~8.8GB
rxnrecer-download-data --models-only    # ~14GB
rxnrecer-download-data --extools-only   # ~13GB

3. Run Prediction

# Basic prediction (S1 mode)
rxnrecer -i input.fasta -o output.tsv -m s1

# Detailed prediction (S2 mode)
rxnrecer -i input.fasta -o output.tsv -m s2

# LLM reasoning (S3 mode, requires API key)
rxnrecer -i input.fasta -o output.json -m s3 -f json

🔧 Usage

Command Line Options

rxnrecer [OPTIONS]

Options:
  -i, --input_fasta    Input FASTA file path (required)
  -o, --output_file    Output file path
  -f, --format         Output format: tsv or json (default: tsv)
  -m, --mode           Prediction mode: s1, s2, or s3 (default: s1)
  -b, --batch_size     Batch size for processing (default: 100)
  -v, --version        Show version

Examples

# Basic usage
rxnrecer -i proteins.fasta -o results.tsv

# Custom batch size
rxnrecer -i proteins.fasta -o results.tsv -b 50

# JSON output
rxnrecer -i proteins.fasta -o results.json -f json

# Use default output path
rxnrecer -i proteins.fasta -m s1

Input Format

FASTA file with protein sequences:

>P12345|Sample protein 1
MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG
>P67890|Sample protein 2
MKLIVWALLLLAAWAVERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG

Output Formats

TSV Output (S1/S2):

input_id	RXNRECer	RXNRECer_with_prob	rxn_details
P12345	RHEA:24076;RHEA:14709	0.9999;0.9999	[reaction details]

JSON Output (S3):

[
  {
    "reaction_id": "RHEA:24076",
    "prediction_confidence": 0.9999,
    "reaction_details": {...}
  }
]

🆕 Advanced Features

Smart Caching

Results are automatically cached for faster repeated predictions:

# Check cache status
rxnrecer-cache status

# Clear cache
rxnrecer-cache clear --all

Data Management

Easy data and model file management:

# Download data
rxnrecer-download-data

# Force re-download
rxnrecer-download-data --force

📁 Project Structure

RXNRECer/                               # Project root (release)
├── .github/                            # CI/CD workflows
│   └── workflows/
├── rxnrecer/                           # Main Python package
│   ├── cli/                            # Command-line interface
│   ├── config/                         # Configuration
│   ├── lib/                            # Core libraries
│   │   ├── datasource/                 # Data source handling
│   │   ├── embedding/                  # Protein embeddings
│   │   ├── evaluation/                 # Evaluation helpers
│   │   ├── llm/                        # Language model integration
│   │   ├── ml/                         # Machine learning utilities
│   │   ├── model/                      # Model architectures
│   │   ├── rxn/                        # Reaction processing
│   │   └── smi/                        # SMILES handling
│   ├── models/                         # Model wrappers
│   └── utils/                          # Utility functions
│
├── extools/                            # External tools (downloaded)
│   ├── ec/                             # EC-related resources
│   └── msa/                            # MSA binaries (e.g., diamond)
│
├── data/                               # Data files (download required)
│   ├── chebi/                          # ChEBI database
│   ├── cpd_svg/                        # Compound SVG files
│   ├── datasets/                       # Training datasets
│   ├── dict/                           # Dictionary files
│   ├── feature_bank/                   # Feature bank
│   ├── rhea/                           # RHEA database
│   ├── rxn_json/                       # Reaction JSON files
│   ├── sample/                         # Sample data
│   └── uniprot/                        # UniProt database
│
├── ckpt/                              # Model checkpoints (download required)
│   ├── esm/                           # ESM models
│   ├── prostt5/                       # ProSTT5 models
│   └── rxnrecer/                      # RXNRECer model files
│
├── results/                            # Output results
│   ├── cache/                          # Prediction cache
│   ├── logs/                           # Log files
│   ├── predictions/                    # Prediction outputs
│   └── sample/                         # Sample results
│
├── docs/                               # Documentation
├── scripts/                            # Build and utility scripts
├── MANIFEST.in                         # Package data manifest
├── pyproject.toml                      # Build and dependencies for PyPI
├── environment_rxnrecer-release.yml    # Conda environment
├── LICENSE                             # MIT License
├── README.md                           # This file
└── .gitignore                          # Git ignore rules

🔧 Configuration

For S3 mode (LLM reasoning), set your API key:

export LLM_API_KEY="your_api_key_here"
export LLM_API_URL="your_api_url_here"

Examples:

# OpenRouter
export LLM_API_KEY="sk-or-v1-your_openrouter_key_here"
export LLM_API_URL="https://openrouter.ai/api/v1"

# OpenAI
export LLM_API_KEY="sk-your_openai_key_here"
export LLM_API_URL="https://api.openai.com/v1"

# Anthropic
export LLM_API_KEY="sk-ant-your_anthropic_key_here"
export LLM_API_URL="https://api.anthropic.com"

Jupyter Notebook Setup

import os
from rxnrecer.config import config as cfg

# Set your API credentials
cfg.LLM_API_KEY = "your_api_key_here"
cfg.LLM_API_URL = "your_api_url_here"

📦 Installation Options

PyPI Installation (Recommended)

pip install rxnrecer

GitHub Installation (Latest)

pip install git+https://github.com/kingstdio/RXNRECer.git

• 🔧 Development: Latest development version
• 🔧 Custom: For advanced users

📚 Documentation

• Installation Guide - Detailed setup instructions
• Release Notes - Version information

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Open a Pull Request

📄 License

MIT License - see LICENSE file for details.

📞 Contact

• Author: Zhenkun Shi
• Email: [e-mail]
• Project: https://github.com/kingstdio/RXNRECer
• PyPI: https://pypi.org/project/rxnrecer/

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🎯 Get started now with: pip install rxnrecer