River Chemical Prediction
Bond-electron matrix reaction prediction system with guaranteed conservation of mass, electrons, and atoms through flow matching transformers
Chemically-Valid Reaction Prediction
River predicts chemical reactions by modeling electron redistribution between atoms using bond-electron matrix representation. Unlike traditional sequence-to-sequence models, River guarantees conservation laws and predicts complete mechanistic pathways.
The system enables efficient fine-tuning for new reaction types and samples multiple pathways to explore alternative reaction routes with full transparency.
Technology Stack
Core Capabilities
Conservation Laws
100% guaranteed conservation of mass, electrons, and atoms by design. No hallucinated atoms or molecules.
Flow Matching Engine
Continuous generative process modeling electron flow with zero-sum constraint for conservation.
BE Matrix Representation
Symmetric matrix encoding electron distribution with diagonal lone pairs and off-diagonal bonds.
Multiple Pathways
Sample and explore alternative reaction routes with configurable temperature and sampling.
Fine-tuning Ready
Adapt to new reaction types with minimal data. Efficient 7M parameter model enables quick training.
Web Interface
Flask-based API and interactive web interface for reaction prediction and pathway analysis.
Technical Specifications
Getting Started
Quick setup guide for River Chemical Prediction
Install Dependencies
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
Initialize Predictor
from river.prediction import ReactionPredictor
predictor = ReactionPredictor()
Predict Reactions
reactants = "CCO.CC(=O)O"
products = predictor.predict(reactants)
print(products['smiles'])
Start Web Interface
python web/app.py
# Access http://localhost:5000
Ready to Predict Chemical Reactions?
Leverage bond-electron matrix flow matching for chemically-valid predictions