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---
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")
checkpoint = torch.load(model_path, map_location='cpu')
```
## 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}}
}
```
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