---
license: mit
tags:
- molecular-generation
- controlnet
- chemistry
- scent-to-molecule
- text-to-smiles
- pytorch
library_name: pytorch
pipeline_tag: text-generation
base_model: molecular-diffusion
language:
- en
datasets:
- sensory-molecules
metrics:
- mse
- bce
model-index:
- name: scent-to-molecule-controlnet
  results:
  - task:
      type: text-to-molecular-generation
      name: Text to Molecular Generation
    dataset:
      type: sensory-molecules
      name: Sensory Molecules Dataset
    metrics:
    - type: validation_loss
      value: 0.030441686697304248
      name: Validation Loss
---

# 🧬 Scent to Molecule Controt

A ControlNet-style model that generates molecular structures (SMILES) from scent descriptions.

## Model Description

This model converts natural language scent descriptions into chemically valid SMILES representations of molecules that would produce those scents.

## Model Details

- **Training epochs**: 20
- **Best validation loss**: 0.030441686697304248
- **Model size**: 2.9 MB
- **Architecture**: ControlNet-style adapter with frozen molecular backbone
- **Text encoder**: sentence-transformers/all-MiniLM-L6-v2

## Usage

```python
from huggingface_hub import hf_hub_download
import torch

# Download model
model_path = hf_hub_download("munchers/scent-to-molecule", "best_control.pt")

model.load_state_dict(checkpoint['model_state_dict'])
if torch.cuda.is_available():
    model = model.cuda()
```

## Examples

| Input Description | Expected Output | Chemical Type |
|------------------|-----------------|---------------|
| "sweet vanilla scent" | Vanillin-like compounds | Phenolic aldehyde |
| "bitter coffee alkaloid" | Caffeine-like compounds | Purine alkaloid |
| "minty cooling fresh" | Menthol-like compounds | Monoterpene alcohol |

## Training Data

- **Training samples**: 815 compounds
- **Validation samples**: 157 compounds  
- **Chemical categories**: 8 (esters, aldehydes, terpenes, phenolics, etc.)

## Limitations

- Uses mock molecular backbone (not full physics simulation)
- Template-based SMILES generation
- English-only descriptions
- Synthetic training dataset

## Citation

```bibtex
@misc{scent-to-molecule-controlnet,
  title={Scent-to-Molecule Control},
  author={Shiva Mudide},
  year={2025},
  howpublished={\url{https://huggingface.co/munchers/scent-to-molecule}}
}
```