README.md

metadata

license: mit
tags:
  - chemistry
  - smiles
  - tokenization
  - dynamic-tokenization
  - h-net
  - hierarchical-networks
  - molecular-representation
  - polymer
  - mamba
  - transformer
datasets:
  - MOSES
language:
  - en
pipeline_tag: feature-extraction

MOSES-2stg

H-Net model for dynamic SMILES tokenization

MOSES molecular dataset, 340M bytes (~5 epochs), 10x concatenation, 2-stage hierarchical architecture

Model Details

Property	Value
Architecture	H-Net (Hierarchical Network)
Parameters	~350M
Dataset	MOSES
Training Bytes	340M
Training Epochs	5
Concatenation	10x SMILES per example
Architecture Variant	2-stage

Architecture Layout

2-stage: ['m4', ['T1m4', ['T22'], 'm4T1'], 'm4']

• Encoder: 4 Mamba blocks for byte-level encoding
• Core: 2-level hierarchical: Stage 0 (T1+4 Mamba) + Stage 1 (22 Transformer blocks)
• Decoder: 4 Mamba blocks for final decoding

Files

• checkpoints/checkpoint_bytes_best.pt - Best checkpoint (lowest validation loss)
• checkpoints/checkpoint_epoch_*.pt - Epoch checkpoints
• metadata.json - Training configuration and history
• test_smiles.txt - Test SMILES used during training
• visualizations/ - Training evolution GIFs and prediction files

Usage

import torch
from pathlib import Path

# Load checkpoint
checkpoint_path = "checkpoints/checkpoint_bytes_best.pt"
checkpoint = torch.load(checkpoint_path, map_location="cpu")

# The checkpoint contains:
# - 'model_state_dict': Model weights
# - 'optimizer_state_dict': Optimizer state
# - 'epoch': Training epoch
# - 'metrics': Training metrics
# - 'cumulative_training_bytes': Total bytes processed

# Load into your H-Net model
# model.load_state_dict(checkpoint['model_state_dict'])

Performance

Tokenization Metrics (from paper)

Metric	Value
Bits-per-byte (BPB)	0.83
Mean token length	2.0

Property Prediction (embeddings)

H-Net embeddings outperform RDKit descriptors on classification tasks:

• BBBP: 0.950 AUC (vs 0.927 for RDKit)
• HIV: 0.788 AUC (vs 0.760 for RDKit)

Citation

@inproceedings{hnet_smiles_2026,
  title={Learning Chemical Grammar: Dynamic Tokenization for SMILES with Hierarchical Networks},
  author={Anonymous},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2026}
}

Related Models

All models from the paper are available:

Polymer (PI1M) Models:

• PI1M-68M - 1 epoch, with concatenation
• PI1M-340M - 5 epochs, with concatenation
• PI1M-1B - 22 epochs, with concatenation (best compression)
• PI1M-nocat - 5 epochs, no concatenation
• PI1M-2stg - 5 epochs, 2-stage architecture

Molecular (MOSES) Models:

• MOSES-340M - 5 epochs, with concatenation
• MOSES-nocat - 5 epochs, no concatenation
• MOSES-2stg - 5 epochs, 2-stage architecture

License

MIT License