Hugging Face Model Integration Plan for Lavoisier

1. Overview

This document outlines the plan to integrate specialized machine learning models from Hugging Face into the Lavoisier project to enhance its capabilities in mass spectrometry analysis, chemical structure prediction, and biomedical knowledge integration.

2. Model Integration Priorities

Phase 1: Core Spectrometry & Chemical Analysis Models

1. MS-ML/SpecTUS_pretrained_only
   • Purpose: Structure reconstruction from EI-MS spectra
   • Integration: Create wrapper in lavoisier/models/spectral_transformers.py
   • Priority: High - Foundational for molecular structure prediction
2. OliXio/CMSSP
   • Purpose: Joint embedding of MS/MS spectra and molecules
   • Integration: Implement in lavoisier/models/embedding_models.py
   • Priority: High - Critical for multi-database search and confidence scoring

Phase 2: Chemical Language Models

1. DeepChem/ChemBERTa-77M-MLM & MTR
   • Purpose: Chemical language modeling for property prediction
   • Integration: Implement in lavoisier/models/chemical_language_models.py
   • Priority: Medium - Enables better property and fragmentation prediction
2. ibm-research/MoLFormer-XL-both-10pct
   • Purpose: SMILES generation and embedding
   • Integration: Add to lavoisier/models/chemical_language_models.py
   • Priority: Medium - Useful for data augmentation

Phase 3: Biomedical Knowledge Integration

1. stanford-crfm/BioMedLM (2.7B)
   • Purpose: Domain-general biomedical LLM
   • Integration: Implement in lavoisier/llm/specialized_llm.py
   • Priority: Medium - Enhances analytical assistance capabilities
2. allenai/scibert_scivocab_uncased
   • Purpose: Scientific text encoding
   • Integration: Add to lavoisier/llm/text_encoders.py
   • Priority: Low - Supports pathway database integration
3. pruas/BENT-PubMedBERT-NER-Chemical
   • Purpose: Chemical entity recognition
   • Integration: Create lavoisier/llm/chemical_ner.py
   • Priority: Low - Improves handling of compound names

Phase 4: Advanced Applications (Future)

1. InstaDeepAI/InstaNovo
   • Purpose: Proteomics support
   • Integration: Create new module lavoisier/proteomics/
   • Priority: Low - Extension beyond current scope

3. Implementation Plan

Step 1: Infrastructure Setup

1. Update requirements.txt to include dependencies:
   • transformers>=4.30.0
   • torch>=2.0.0
   • datasets>=2.14.0
   • huggingface_hub>=0.17.0
2. Create model registry module (lavoisier/models/registry.py):
   • Implement model caching and version management
   • Support local and remote model loading
   • Add progress tracking for downloads

Step 2: Core Model Integration Framework

1. Create base model wrapper classes:
   • BaseHuggingFaceModel - Common functionality for all models
   • SpectralModel - Specific to MS analysis models
   • ChemicalLanguageModel - For chemical-specific language models
   • BiomedicalTextModel - For text-based models
2. Implement model loading utilities:
   • Automatic caching
   • Offline mode support
   • Configuration management

Step 3: Model-Specific Implementations

1. Implement each model wrapper according to priority
2. Create integration tests for each model
3. Add documentation and example workflows

Step 4: Pipeline Integration

1. Update the Orchestrator to support model selection
2. Create new pipeline types that leverage these models
3. Implement fallback mechanisms for when models are unavailable

4. Implementation Timeline

• Phase 1: 1-2 weeks
• Phase 2: 1-2 weeks
• Phase 3: 2-3 weeks
• Phase 4: Future work

5. Dependencies

• PyTorch
• Transformers
• HuggingFace Hub
• Additional model-specific dependencies

6. Testing Strategy

1. Unit tests for each model wrapper
2. Integration tests with sample data
3. Performance benchmarks
4. Offline functionality tests