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--- |
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title: PFAS AI Analyzer |
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emoji: 🧪 |
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colorFrom: red |
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colorTo: indigo |
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sdk: docker |
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app_port: 7860 |
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tags: |
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- chemistry |
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- pfas |
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- bert |
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- toxicology |
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- streamlit |
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pinned: false |
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short_description: AI-powered PFAS detection and risk assessment pipeline. |
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--- |
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# 🧪 PFAS AI Analyzer (BERT Enhanced) |
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This application is an end-to-end AI pipeline designed to identify, classify, and assess the environmental risks of **Per- and Polyfluoroalkyl Substances (PFAS)**. |
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It leverages a fine-tuned **BERT (Bidirectional Encoder Representations from Transformers)** model to generate molecular embeddings, followed by Random Forest regressors for property prediction. |
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## 🚀 Key Features |
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1. **Advanced PFAS Detection:** Uses the OECD-aligned "Chain Rule" logic to distinguish industrial PFAS from fluorinated pharmaceuticals (e.g., Prozac, Fipronil). |
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2. **Subclass Classification:** Automatically categorizes molecules into PFCA, PFSA, or General PFAS. |
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3. **Risk Assessment:** Predicts key environmental properties: |
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* **Persistence:** Estimated half-life / biodegradation potential. |
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* **Mobility:** Soil adsorption coefficient ($K_{oc}$). |
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* **Bioaccumulation:** Bioconcentration factor (BCF) / LogP. |
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4. **BERT Embeddings:** Utilizes a transformer model trained on ChEMBL data to understand deep molecular features beyond simple fingerprints. |
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## 🧠 How It Works |
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1. **Input:** The user provides a SMILES string (Simplified Molecular Input Line Entry System). |
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2. **Tokenization:** The SMILES string is tokenized using a specialized `Spe_Tokenizer`. |
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3. **Embedding:** The **SMILE-to-BERT** model converts the tokens into a 113-dimensional dense vector representation. |
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4. **Inference:** |
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* A **Random Forest Classifier** determines the PFAS subclass. |
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* **Random Forest Regressors** predict environmental properties. |
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5. **Validation:** A rule-based sanity checker applies chemical structure rules to prevent false positives. |
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## 📂 File Structure |
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* `src/app.py`: Main Streamlit application. |
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* `src/pfas_assets.zip`: Contains the BERT model weights and tokenizer data. |
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* `src/*.pkl`: Trained Scikit-Learn models |
