README.md

metadata

title: PFAS AI Analyzer
emoji: 🧪
colorFrom: red
colorTo: indigo
sdk: docker
app_port: 7860
tags:
  - chemistry
  - pfas
  - bert
  - toxicology
  - streamlit
pinned: false
short_description: AI-powered PFAS detection and risk assessment pipeline.

🧪 PFAS AI Analyzer (BERT Enhanced)

This application is an end-to-end AI pipeline designed to identify, classify, and assess the environmental risks of Per- and Polyfluoroalkyl Substances (PFAS).

It leverages a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model to generate molecular embeddings, followed by Random Forest regressors for property prediction.

🚀 Key Features

1. Advanced PFAS Detection: Uses the OECD-aligned "Chain Rule" logic to distinguish industrial PFAS from fluorinated pharmaceuticals (e.g., Prozac, Fipronil).
2. Subclass Classification: Automatically categorizes molecules into PFCA, PFSA, or General PFAS.
3. Risk Assessment: Predicts key environmental properties:
   • Persistence: Estimated half-life / biodegradation potential.
   • Mobility: Soil adsorption coefficient ($K_{oc}$).
   • Bioaccumulation: Bioconcentration factor (BCF) / LogP.
4. BERT Embeddings: Utilizes a transformer model trained on ChEMBL data to understand deep molecular features beyond simple fingerprints.

🧠 How It Works

1. Input: The user provides a SMILES string (Simplified Molecular Input Line Entry System).
2. Tokenization: The SMILES string is tokenized using a specialized Spe_Tokenizer.
3. Embedding: The SMILE-to-BERT model converts the tokens into a 113-dimensional dense vector representation.
4. Inference:
   • A Random Forest Classifier determines the PFAS subclass.
   • Random Forest Regressors predict environmental properties.
5. Validation: A rule-based sanity checker applies chemical structure rules to prevent false positives.

📂 File Structure

• src/app.py: Main Streamlit application.
• src/pfas_assets.zip: Contains the BERT model weights and tokenizer data.
• src/*.pkl: Trained Scikit-Learn models