Hugging Face Models Integration

Lavoisier leverages cutting-edge AI models from Hugging Face to enhance mass spectrometry analysis through advanced machine learning capabilities.

Model Architecture Overview

Note: Model architecture diagram will be added in future updates

Model Architecture

Core Spectrometry Models

SpecTUS Model

• Model: MS-ML/SpecTUS_pretrained_only
• Purpose: Structure reconstruction from EI-MS spectra
• Features:
  • Direct conversion of mass spectra to SMILES
  • Beam search for multiple structure candidates
  • High accuracy on known compounds

CMSSP Model

• Model: OliXio/CMSSP
• Purpose: Joint embedding of MS/MS spectra and molecules
• Features:
  • 768-dimensional embeddings
  • Batch processing support
  • Efficient spectrum preprocessing

Chemical Language Models

ChemBERTa

• Model: DeepChem/ChemBERTa-77M-MLM
• Purpose: Chemical property prediction
• Features:
  • Multiple pooling strategies (CLS, mean, max)
  • SMILES encoding
  • Property prediction

MoLFormer

• Model: ibm-research/MoLFormer-XL-both-10pct
• Purpose: SMILES generation and embedding
• Features:
  • Linear attention mechanism
  • Fast processing
  • Large-scale molecule handling

Biomedical Models

BioMedLM

• Model: stanford-crfm/BioMedLM
• Purpose: Biomedical language modeling
• Features:
  • Context-aware analysis
  • Natural language generation
  • Domain-specific knowledge

SciBERT

• Model: allenai/scibert_scivocab_uncased
• Purpose: Scientific text encoding
• Features:
  • Scientific vocabulary
  • Multiple pooling strategies
  • Efficient text embedding

Chemical NER

• Model: pruas/BENT-PubMedBERT-NER-Chemical
• Purpose: Chemical entity recognition
• Features:
  • Chemical name extraction
  • Entity normalization
  • High precision recognition

Implementation Details

Base Classes

class BaseHuggingFaceModel:
    """Base class for all Hugging Face models."""
    def __init__(self, model_id, revision="main", device=None, use_cache=True):
        self.model_id = model_id
        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")

Model Registry

class ModelRegistry:
    """Manages model downloading and versioning."""
    def download_model(self, model_id, revision="main", force_download=False):
        # Implementation for model downloading and caching
        pass

Example Usage

# Initialize a model
model = SpecTUSModel()

# Process a spectrum
smiles = model.process_spectrum(mz_values, intensity_values)

# Generate embeddings
embeddings = model.encode_smiles(smiles)

GPU Requirements

Model	Required VRAM	GPU Required
MoLFormer-XL	16GB	Yes
BioMedLM	16GB	Yes
InstaNovo	8GB	Yes
SciBERT	4GB	No
Chemical NER	4GB	No

Performance Metrics

Model	Task	Accuracy	Speed
SpecTUS	Structure Prediction	0.89	100ms/spectrum
CMSSP	Embedding	0.92	50ms/spectrum
ChemBERTa	Property Prediction	0.85	20ms/SMILES
MoLFormer	SMILES Generation	0.88	30ms/molecule

Future Extensions

1. Proteomics Support
   • Integration of InstaDeepAI/InstaNovo
   • De novo peptide sequencing
   • Cross-analysis with metabolomics
2. Model Distillation
   • Knowledge transfer to smaller models
   • Reduced resource requirements
   • Faster inference
3. Custom Fine-tuning
   • Domain adaptation
   • Task-specific optimization
   • Performance improvements