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ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmolfeat\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25],"features","molecular","for","ml","convert","models","SMILES","Molecular ML","2026-11-10 10:12:30",false,"---\nname: molfeat\ndescription: \"Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.\"\n---\n\n# Molfeat - Molecular Featurization Hub\n\n## Overview\n\nMolfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.\n\n## When to Use This Skill\n\nThis skill should be used when working with:\n- **Molecular machine learning**: Building QSAR\u002FQSPR models, property prediction\n- **Virtual screening**: Ranking compound libraries for biological activity\n- **Similarity searching**: Finding structurally similar molecules\n- **Chemical space analysis**: Clustering, visualization, dimensionality reduction\n- **Deep learning**: Training neural networks on molecular data\n- **Featurization pipelines**: Converting SMILES to ML-ready representations\n- **Cheminformatics**: Any task requiring molecular feature extraction\n\n## Installation\n\n```bash\nuv pip install molfeat\n\n# With all optional dependencies\nuv pip install \"molfeat[all]\"\n```\n\n**Optional dependencies for specific featurizers:**\n- `molfeat[dgl]` - GNN models (GIN variants)\n- `molfeat[graphormer]` - Graphormer models\n- `molfeat[transformer]` - ChemBERTa, ChemGPT, MolT5\n- `molfeat[fcd]` - FCD descriptors\n- `molfeat[map4]` - MAP4 fingerprints\n\n## Core Concepts\n\nMolfeat organizes featurization into three hierarchical classes:\n\n### 1. Calculators (`molfeat.calc`)\n\nCallable objects that convert individual molecules into feature vectors. Accept RDKit `Chem.Mol` objects or SMILES strings.\n\n**Use calculators for:**\n- Single molecule featurization\n- Custom processing loops\n- Direct feature computation\n\n**Example:**\n```python\nfrom molfeat.calc import FPCalculator\n\ncalc = FPCalculator(\"ecfp\", radius=3, fpSize=2048)\nfeatures = calc(\"CCO\")  # Returns numpy array (2048,)\n```\n\n### 2. Transformers (`molfeat.trans`)\n\nScikit-learn compatible transformers that wrap calculators for batch processing with parallelization.\n\n**Use transformers for:**\n- Batch featurization of molecular datasets\n- Integration with scikit-learn pipelines\n- Parallel processing (automatic CPU utilization)\n\n**Example:**\n```python\nfrom molfeat.trans import MoleculeTransformer\nfrom molfeat.calc import FPCalculator\n\ntransformer = MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\nfeatures = transformer(smiles_list)  # Parallel processing\n```\n\n### 3. Pretrained Transformers (`molfeat.trans.pretrained`)\n\nSpecialized transformers for deep learning models with batched inference and caching.\n\n**Use pretrained transformers for:**\n- State-of-the-art molecular embeddings\n- Transfer learning from large chemical datasets\n- Deep learning feature extraction\n\n**Example:**\n```python\nfrom molfeat.trans.pretrained import PretrainedMolTransformer\n\ntransformer = PretrainedMolTransformer(\"ChemBERTa-77M-MLM\", n_jobs=-1)\nembeddings = transformer(smiles_list)  # Deep learning embeddings\n```\n\n## Quick Start Workflow\n\n### Basic Featurization\n\n```python\nimport datamol as dm\nfrom molfeat.calc import FPCalculator\nfrom molfeat.trans import MoleculeTransformer\n\n# Load molecular data\nsmiles = [\"CCO\", \"CC(=O)O\", \"c1ccccc1\", \"CC(C)O\"]\n\n# Create calculator and transformer\ncalc = FPCalculator(\"ecfp\", radius=3)\ntransformer = MoleculeTransformer(calc, n_jobs=-1)\n\n# Featurize molecules\nfeatures = transformer(smiles)\nprint(f\"Shape: {features.shape}\")  # (4, 2048)\n```\n\n### Save and Load Configuration\n\n```python\n# Save featurizer configuration for reproducibility\ntransformer.to_state_yaml_file(\"featurizer_config.yml\")\n\n# Reload exact configuration\nloaded = MoleculeTransformer.from_state_yaml_file(\"featurizer_config.yml\")\n```\n\n### Handle Errors Gracefully\n\n```python\n# Process dataset with potentially invalid SMILES\ntransformer = MoleculeTransformer(\n    calc,\n    n_jobs=-1,\n    ignore_errors=True,  # Continue on failures\n    verbose=True          # Log error details\n)\n\nfeatures = transformer(smiles_with_errors)\n# Returns None for failed molecules\n```\n\n## Choosing the Right Featurizer\n\n### For Traditional Machine Learning (RF, SVM, XGBoost)\n\n**Start with fingerprints:**\n```python\n# ECFP - Most popular, general-purpose\nFPCalculator(\"ecfp\", radius=3, fpSize=2048)\n\n# MACCS - Fast, good for scaffold hopping\nFPCalculator(\"maccs\")\n\n# MAP4 - Efficient for large-scale screening\nFPCalculator(\"map4\")\n```\n\n**For interpretable models:**\n```python\n# RDKit 2D descriptors (200+ named properties)\nfrom molfeat.calc import RDKitDescriptors2D\nRDKitDescriptors2D()\n\n# Mordred (1800+ comprehensive descriptors)\nfrom molfeat.calc import MordredDescriptors\nMordredDescriptors()\n```\n\n**Combine multiple featurizers:**\n```python\nfrom molfeat.trans import FeatConcat\n\nconcat = FeatConcat([\n    FPCalculator(\"maccs\"),      # 167 dimensions\n    FPCalculator(\"ecfp\")         # 2048 dimensions\n])  # Result: 2215-dimensional combined features\n```\n\n### For Deep Learning\n\n**Transformer-based embeddings:**\n```python\n# ChemBERTa - Pre-trained on 77M PubChem compounds\nPretrainedMolTransformer(\"ChemBERTa-77M-MLM\")\n\n# ChemGPT - Autoregressive language model\nPretrainedMolTransformer(\"ChemGPT-1.2B\")\n```\n\n**Graph neural networks:**\n```python\n# GIN models with different pre-training objectives\nPretrainedMolTransformer(\"gin-supervised-masking\")\nPretrainedMolTransformer(\"gin-supervised-infomax\")\n\n# Graphormer for quantum chemistry\nPretrainedMolTransformer(\"Graphormer-pcqm4mv2\")\n```\n\n### For Similarity Searching\n\n```python\n# ECFP - General purpose, most widely used\nFPCalculator(\"ecfp\")\n\n# MACCS - Fast, scaffold-based similarity\nFPCalculator(\"maccs\")\n\n# MAP4 - Efficient for large databases\nFPCalculator(\"map4\")\n\n# USR\u002FUSRCAT - 3D shape similarity\nfrom molfeat.calc import USRDescriptors\nUSRDescriptors()\n```\n\n### For Pharmacophore-Based Approaches\n\n```python\n# FCFP - Functional group based\nFPCalculator(\"fcfp\")\n\n# CATS - Pharmacophore pair distributions\nfrom molfeat.calc import CATSCalculator\nCATSCalculator(mode=\"2D\")\n\n# Gobbi - Explicit pharmacophore features\nFPCalculator(\"gobbi2D\")\n```\n\n## Common Workflows\n\n### Building a QSAR Model\n\n```python\nfrom molfeat.trans import MoleculeTransformer\nfrom molfeat.calc import FPCalculator\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import cross_val_score\n\n# Featurize molecules\ntransformer = MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\nX = transformer(smiles_train)\n\n# Train model\nmodel = RandomForestRegressor(n_estimators=100)\nscores = cross_val_score(model, X, y_train, cv=5)\nprint(f\"R² = {scores.mean():.3f}\")\n\n# Save configuration for deployment\ntransformer.to_state_yaml_file(\"production_featurizer.yml\")\n```\n\n### Virtual Screening Pipeline\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Train on known actives\u002Finactives\ntransformer = MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\nX_train = transformer(train_smiles)\nclf = RandomForestClassifier(n_estimators=500)\nclf.fit(X_train, train_labels)\n\n# Screen large library\nX_screen = transformer(screening_library)  # e.g., 1M compounds\npredictions = clf.predict_proba(X_screen)[:, 1]\n\n# Rank and select top hits\ntop_indices = predictions.argsort()[::-1][:1000]\ntop_hits = [screening_library[i] for i in top_indices]\n```\n\n### Similarity Search\n\n```python\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# Query molecule\ncalc = FPCalculator(\"ecfp\")\nquery_fp = calc(query_smiles).reshape(1, -1)\n\n# Database fingerprints\ntransformer = MoleculeTransformer(calc, n_jobs=-1)\ndatabase_fps = transformer(database_smiles)\n\n# Compute similarity\nsimilarities = cosine_similarity(query_fp, database_fps)[0]\ntop_similar = similarities.argsort()[-10:][::-1]\n```\n\n### Scikit-learn Pipeline Integration\n\n```python\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Create end-to-end pipeline\npipeline = Pipeline([\n    ('featurizer', MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)),\n    ('classifier', RandomForestClassifier(n_estimators=100))\n])\n\n# Train and predict directly on SMILES\npipeline.fit(smiles_train, y_train)\npredictions = pipeline.predict(smiles_test)\n```\n\n### Comparing Multiple Featurizers\n\n```python\nfeaturizers = {\n    'ECFP': FPCalculator(\"ecfp\"),\n    'MACCS': FPCalculator(\"maccs\"),\n    'Descriptors': RDKitDescriptors2D(),\n    'ChemBERTa': PretrainedMolTransformer(\"ChemBERTa-77M-MLM\")\n}\n\nresults = {}\nfor name, feat in featurizers.items():\n    transformer = MoleculeTransformer(feat, n_jobs=-1)\n    X = transformer(smiles)\n    # Evaluate with your ML model\n    score = evaluate_model(X, y)\n    results[name] = score\n```\n\n## Discovering Available Featurizers\n\nUse the ModelStore to explore all available featurizers:\n\n```python\nfrom molfeat.store.modelstore import ModelStore\n\nstore = ModelStore()\n\n# List all available models\nall_models = store.available_models\nprint(f\"Total featurizers: {len(all_models)}\")\n\n# Search for specific models\nchemberta_models = store.search(name=\"ChemBERTa\")\nfor model in chemberta_models:\n    print(f\"- {model.name}: {model.description}\")\n\n# Get usage information\nmodel_card = store.search(name=\"ChemBERTa-77M-MLM\")[0]\nmodel_card.usage()  # Display usage examples\n\n# Load model\ntransformer = store.load(\"ChemBERTa-77M-MLM\")\n```\n\n## Advanced Features\n\n### Custom Preprocessing\n\n```python\nclass CustomTransformer(MoleculeTransformer):\n    def preprocess(self, mol):\n        \"\"\"Custom preprocessing pipeline\"\"\"\n        if isinstance(mol, str):\n            mol = dm.to_mol(mol)\n        mol = dm.standardize_mol(mol)\n        mol = dm.remove_salts(mol)\n        return mol\n\ntransformer = CustomTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\n```\n\n### Batch Processing Large Datasets\n\n```python\ndef featurize_in_chunks(smiles_list, transformer, chunk_size=10000):\n    \"\"\"Process large datasets in chunks to manage memory\"\"\"\n    all_features = []\n    for i in range(0, len(smiles_list), chunk_size):\n        chunk = smiles_list[i:i+chunk_size]\n        features = transformer(chunk)\n        all_features.append(features)\n    return np.vstack(all_features)\n```\n\n### Caching Expensive Embeddings\n\n```python\nimport pickle\n\ncache_file = \"embeddings_cache.pkl\"\ntransformer = PretrainedMolTransformer(\"ChemBERTa-77M-MLM\", n_jobs=-1)\n\ntry:\n    with open(cache_file, \"rb\") as f:\n        embeddings = pickle.load(f)\nexcept FileNotFoundError:\n    embeddings = transformer(smiles_list)\n    with open(cache_file, \"wb\") as f:\n        pickle.dump(embeddings, f)\n```\n\n## Performance Tips\n\n1. **Use parallelization**: Set `n_jobs=-1` to utilize all CPU cores\n2. **Batch processing**: Process multiple molecules at once instead of loops\n3. **Choose appropriate featurizers**: Fingerprints are faster than deep learning models\n4. **Cache pretrained models**: Leverage built-in caching for repeated use\n5. **Use float32**: Set `dtype=np.float32` when precision allows\n6. **Handle errors efficiently**: Use `ignore_errors=True` for large datasets\n\n## Common Featurizers Reference\n\n**Quick reference for frequently used featurizers:**\n\n| Featurizer | Type | Dimensions | Speed | Use Case |\n|------------|------|------------|-------|----------|\n| `ecfp` | Fingerprint | 2048 | Fast | General purpose |\n| `maccs` | Fingerprint | 167 | Very fast | Scaffold similarity |\n| `desc2D` | Descriptors | 200+ | Fast | Interpretable models |\n| `mordred` | Descriptors | 1800+ | Medium | Comprehensive features |\n| `map4` | Fingerprint | 1024 | Fast | Large-scale screening |\n| `ChemBERTa-77M-MLM` | Deep learning | 768 | Slow* | Transfer learning |\n| `gin-supervised-masking` | GNN | Variable | Slow* | Graph-based models |\n\n*First run is slow; subsequent runs benefit from caching\n\n## Resources\n\nThis skill includes comprehensive reference documentation:\n\n### references\u002Fapi_reference.md\nComplete API documentation covering:\n- `molfeat.calc` - All calculator classes and parameters\n- `molfeat.trans` - Transformer classes and methods\n- `molfeat.store` - ModelStore usage\n- Common patterns and integration examples\n- Performance optimization tips\n\n**When to load:** Reference when implementing specific calculators, understanding transformer parameters, or integrating with scikit-learn\u002FPyTorch.\n\n### references\u002Favailable_featurizers.md\nComprehensive catalog of all 100+ featurizers organized by category:\n- Transformer-based language models (ChemBERTa, ChemGPT)\n- Graph neural networks (GIN, Graphormer)\n- Molecular descriptors (RDKit, Mordred)\n- Fingerprints (ECFP, MACCS, MAP4, and 15+ others)\n- Pharmacophore descriptors (CATS, Gobbi)\n- Shape descriptors (USR, ElectroShape)\n- Scaffold-based descriptors\n\n**When to load:** Reference when selecting the optimal featurizer for a specific task, exploring available options, or understanding featurizer characteristics.\n\n**Search tip:** Use grep to find specific featurizer types:\n```bash\ngrep -i \"chembert\" references\u002Favailable_featurizers.md\ngrep -i \"pharmacophore\" references\u002Favailable_featurizers.md\n```\n\n### references\u002Fexamples.md\nPractical code examples for common scenarios:\n- Installation and quick start\n- Calculator and transformer examples\n- Pretrained model usage\n- Scikit-learn and PyTorch integration\n- Virtual screening workflows\n- QSAR model building\n- Similarity searching\n- Troubleshooting and best practices\n\n**When to load:** Reference when implementing specific workflows, troubleshooting issues, or learning molfeat patterns.\n\n## Troubleshooting\n\n### Invalid Molecules\nEnable error handling to skip invalid SMILES:\n```python\ntransformer = MoleculeTransformer(\n    calc,\n    ignore_errors=True,\n    verbose=True\n)\n```\n\n### Memory Issues with Large Datasets\nProcess in chunks or use streaming approaches for datasets > 100K molecules.\n\n### Pretrained Model Dependencies\nSome models require additional packages. Install specific extras:\n```bash\nuv pip install \"molfeat[transformer]\"  # For ChemBERTa\u002FChemGPT\nuv pip install \"molfeat[dgl]\"          # For GIN models\n```\n\n### Reproducibility\nSave exact configurations and document versions:\n```python\ntransformer.to_state_yaml_file(\"config.yml\")\nimport molfeat\nprint(f\"molfeat version: {molfeat.__version__}\")\n```\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fmolfeat-docs.datamol.io\u002F\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Fdatamol-io\u002Fmolfeat\n- **PyPI Package**: https:\u002F\u002Fpypi.org\u002Fproject\u002Fmolfeat\u002F\n- **Tutorial**: https:\u002F\u002Fportal.valencelabs.com\u002Fdatamol\u002Fpost\u002Ftypes-of-featurizers-b1e8HHrbFMkbun6\n","# Molfeat - Molecular Featurization Hub\n\n## Overview\n\nMolfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.\n\n## When to Use This Skill\n\nThis skill should be used when working with:\n- **Molecular machine learning**: Building QSAR\u002FQSPR models, property prediction\n- **Virtual screening**: Ranking compound libraries for biological activity\n- **Similarity searching**: Finding structurally similar molecules\n- **Chemical space analysis**: Clustering, visualization, dimensionality reduction\n- **Deep learning**: Training neural networks on molecular data\n- **Featurization pipelines**: Converting SMILES to ML-ready representations\n- **Cheminformatics**: Any task requiring molecular feature extraction\n\n## Installation\n\n```bash\nuv pip install molfeat\n\n# With all optional dependencies\nuv pip install \"molfeat[all]\"\n```\n\n**Optional dependencies for specific featurizers:**\n- `molfeat[dgl]` - GNN models (GIN variants)\n- `molfeat[graphormer]` - Graphormer models\n- `molfeat[transformer]` - ChemBERTa, ChemGPT, MolT5\n- `molfeat[fcd]` - FCD descriptors\n- `molfeat[map4]` - MAP4 fingerprints\n\n## Core Concepts\n\nMolfeat organizes featurization into three hierarchical classes:\n\n### 1. Calculators (`molfeat.calc`)\n\nCallable objects that convert individual molecules into feature vectors. Accept RDKit `Chem.Mol` objects or SMILES strings.\n\n**Use calculators for:**\n- Single molecule featurization\n- Custom processing loops\n- Direct feature computation\n\n**Example:**\n```python\nfrom molfeat.calc import FPCalculator\n\ncalc = FPCalculator(\"ecfp\", radius=3, fpSize=2048)\nfeatures = calc(\"CCO\")  # Returns numpy array (2048,)\n```\n\n### 2. Transformers (`molfeat.trans`)\n\nScikit-learn compatible transformers that wrap calculators for batch processing with parallelization.\n\n**Use transformers for:**\n- Batch featurization of molecular datasets\n- Integration with scikit-learn pipelines\n- Parallel processing (automatic CPU utilization)\n\n**Example:**\n```python\nfrom molfeat.trans import MoleculeTransformer\nfrom molfeat.calc import FPCalculator\n\ntransformer = MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\nfeatures = transformer(smiles_list)  # Parallel processing\n```\n\n### 3. Pretrained Transformers (`molfeat.trans.pretrained`)\n\nSpecialized transformers for deep learning models with batched inference and caching.\n\n**Use pretrained transformers for:**\n- State-of-the-art molecular embeddings\n- Transfer learning from large chemical datasets\n- Deep learning feature extraction\n\n**Example:**\n```python\nfrom molfeat.trans.pretrained import PretrainedMolTransformer\n\ntransformer = PretrainedMolTransformer(\"ChemBERTa-77M-MLM\", n_jobs=-1)\nembeddings = transformer(smiles_list)  # Deep learning embeddings\n```\n\n## Quick Start Workflow\n\n### Basic Featurization\n\n```python\nimport datamol as dm\nfrom molfeat.calc import FPCalculator\nfrom molfeat.trans import MoleculeTransformer\n\n# Load molecular data\nsmiles = [\"CCO\", \"CC(=O)O\", \"c1ccccc1\", \"CC(C)O\"]\n\n# Create calculator and transformer\ncalc = FPCalculator(\"ecfp\", radius=3)\ntransformer = MoleculeTransformer(calc, n_jobs=-1)\n\n# Featurize molecules\nfeatures = transformer(smiles)\nprint(f\"Shape: {features.shape}\")  # (4, 2048)\n```\n\n### Save and Load Configuration\n\n```python\n# Save featurizer configuration for reproducibility\ntransformer.to_state_yaml_file(\"featurizer_config.yml\")\n\n# Reload exact configuration\nloaded = MoleculeTransformer.from_state_yaml_file(\"featurizer_config.yml\")\n```\n\n### Handle Errors Gracefully\n\n```python\n# Process dataset with potentially invalid SMILES\ntransformer = MoleculeTransformer(\n    calc,\n    n_jobs=-1,\n    ignore_errors=True,  # Continue on failures\n    verbose=True          # Log error details\n)\n\nfeatures = transformer(smiles_with_errors)\n# Returns None for failed molecules\n```\n\n## Choosing the Right Featurizer\n\n### For Traditional Machine Learning (RF, SVM, XGBoost)\n\n**Start with fingerprints:**\n```python\n# ECFP - Most popular, general-purpose\nFPCalculator(\"ecfp\", radius=3, fpSize=2048)\n\n# MACCS - Fast, good for scaffold hopping\nFPCalculator(\"maccs\")\n\n# MAP4 - Efficient for large-scale screening\nFPCalculator(\"map4\")\n```\n\n**For interpretable models:**\n```python\n# RDKit 2D descriptors (200+ named properties)\nfrom molfeat.calc import RDKitDescriptors2D\nRDKitDescriptors2D()\n\n# Mordred (1800+ comprehensive descriptors)\nfrom molfeat.calc import MordredDescriptors\nMordredDescriptors()\n```\n\n**Combine multiple featurizers:**\n```python\nfrom molfeat.trans import FeatConcat\n\nconcat = FeatConcat([\n    FPCalculator(\"maccs\"),      # 167 dimensions\n    FPCalculator(\"ecfp\")         # 2048 dimensions\n])  # Result: 2215-dimensional combined features\n```\n\n### For Deep Learning\n\n**Transformer-based embeddings:**\n```python\n# ChemBERTa - Pre-trained on 77M PubChem compounds\nPretrainedMolTransformer(\"ChemBERTa-77M-MLM\")\n\n# ChemGPT - Autoregressive language model\nPretrainedMolTransformer(\"ChemGPT-1.2B\")\n```\n\n**Graph neural networks:**\n```python\n# GIN models with different pre-training objectives\nPretrainedMolTransformer(\"gin-supervised-masking\")\nPretrainedMolTransformer(\"gin-supervised-infomax\")\n\n# Graphormer for quantum chemistry\nPretrainedMolTransformer(\"Graphormer-pcqm4mv2\")\n```\n\n### For Similarity Searching\n\n```python\n# ECFP - General purpose, most widely used\nFPCalculator(\"ecfp\")\n\n# MACCS - Fast, scaffold-based similarity\nFPCalculator(\"maccs\")\n\n# MAP4 - Efficient for large databases\nFPCalculator(\"map4\")\n\n# USR\u002FUSRCAT - 3D shape similarity\nfrom molfeat.calc import USRDescriptors\nUSRDescriptors()\n```\n\n### For Pharmacophore-Based Approaches\n\n```python\n# FCFP - Functional group based\nFPCalculator(\"fcfp\")\n\n# CATS - Pharmacophore pair distributions\nfrom molfeat.calc import CATSCalculator\nCATSCalculator(mode=\"2D\")\n\n# Gobbi - Explicit pharmacophore features\nFPCalculator(\"gobbi2D\")\n```\n\n## Common Workflows\n\n### Building a QSAR Model\n\n```python\nfrom molfeat.trans import MoleculeTransformer\nfrom molfeat.calc import FPCalculator\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import cross_val_score\n\n# Featurize molecules\ntransformer = MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\nX = transformer(smiles_train)\n\n# Train model\nmodel = RandomForestRegressor(n_estimators=100)\nscores = cross_val_score(model, X, y_train, cv=5)\nprint(f\"R² = {scores.mean():.3f}\")\n\n# Save configuration for deployment\ntransformer.to_state_yaml_file(\"production_featurizer.yml\")\n```\n\n### Virtual Screening Pipeline\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Train on known actives\u002Finactives\ntransformer = MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\nX_train = transformer(train_smiles)\nclf = RandomForestClassifier(n_estimators=500)\nclf.fit(X_train, train_labels)\n\n# Screen large library\nX_screen = transformer(screening_library)  # e.g., 1M compounds\npredictions = clf.predict_proba(X_screen)[:, 1]\n\n# Rank and select top hits\ntop_indices = predictions.argsort()[::-1][:1000]\ntop_hits = [screening_library[i] for i in top_indices]\n```\n\n### Similarity Search\n\n```python\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# Query molecule\ncalc = FPCalculator(\"ecfp\")\nquery_fp = calc(query_smiles).reshape(1, -1)\n\n# Database fingerprints\ntransformer = MoleculeTransformer(calc, n_jobs=-1)\ndatabase_fps = transformer(database_smiles)\n\n# Compute similarity\nsimilarities = cosine_similarity(query_fp, database_fps)[0]\ntop_similar = similarities.argsort()[-10:][::-1]\n```\n\n### Scikit-learn Pipeline Integration\n\n```python\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Create end-to-end pipeline\npipeline = Pipeline([\n    ('featurizer', MoleculeTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)),\n    ('classifier', RandomForestClassifier(n_estimators=100))\n])\n\n# Train and predict directly on SMILES\npipeline.fit(smiles_train, y_train)\npredictions = pipeline.predict(smiles_test)\n```\n\n### Comparing Multiple Featurizers\n\n```python\nfeaturizers = {\n    'ECFP': FPCalculator(\"ecfp\"),\n    'MACCS': FPCalculator(\"maccs\"),\n    'Descriptors': RDKitDescriptors2D(),\n    'ChemBERTa': PretrainedMolTransformer(\"ChemBERTa-77M-MLM\")\n}\n\nresults = {}\nfor name, feat in featurizers.items():\n    transformer = MoleculeTransformer(feat, n_jobs=-1)\n    X = transformer(smiles)\n    # Evaluate with your ML model\n    score = evaluate_model(X, y)\n    results[name] = score\n```\n\n## Discovering Available Featurizers\n\nUse the ModelStore to explore all available featurizers:\n\n```python\nfrom molfeat.store.modelstore import ModelStore\n\nstore = ModelStore()\n\n# List all available models\nall_models = store.available_models\nprint(f\"Total featurizers: {len(all_models)}\")\n\n# Search for specific models\nchemberta_models = store.search(name=\"ChemBERTa\")\nfor model in chemberta_models:\n    print(f\"- {model.name}: {model.description}\")\n\n# Get usage information\nmodel_card = store.search(name=\"ChemBERTa-77M-MLM\")[0]\nmodel_card.usage()  # Display usage examples\n\n# Load model\ntransformer = store.load(\"ChemBERTa-77M-MLM\")\n```\n\n## Advanced Features\n\n### Custom Preprocessing\n\n```python\nclass CustomTransformer(MoleculeTransformer):\n    def preprocess(self, mol):\n        \"\"\"Custom preprocessing pipeline\"\"\"\n        if isinstance(mol, str):\n            mol = dm.to_mol(mol)\n        mol = dm.standardize_mol(mol)\n        mol = dm.remove_salts(mol)\n        return mol\n\ntransformer = CustomTransformer(FPCalculator(\"ecfp\"), n_jobs=-1)\n```\n\n### Batch Processing Large Datasets\n\n```python\ndef featurize_in_chunks(smiles_list, transformer, chunk_size=10000):\n    \"\"\"Process large datasets in chunks to manage memory\"\"\"\n    all_features = []\n    for i in range(0, len(smiles_list), chunk_size):\n        chunk = smiles_list[i:i+chunk_size]\n        features = transformer(chunk)\n        all_features.append(features)\n    return np.vstack(all_features)\n```\n\n### Caching Expensive Embeddings\n\n```python\nimport pickle\n\ncache_file = \"embeddings_cache.pkl\"\ntransformer = PretrainedMolTransformer(\"ChemBERTa-77M-MLM\", n_jobs=-1)\n\ntry:\n    with open(cache_file, \"rb\") as f:\n        embeddings = pickle.load(f)\nexcept FileNotFoundError:\n    embeddings = transformer(smiles_list)\n    with open(cache_file, \"wb\") as f:\n        pickle.dump(embeddings, f)\n```\n\n## Performance Tips\n\n1. **Use parallelization**: Set `n_jobs=-1` to utilize all CPU cores\n2. **Batch processing**: Process multiple molecules at once instead of loops\n3. **Choose appropriate featurizers**: Fingerprints are faster than deep learning models\n4. **Cache pretrained models**: Leverage built-in caching for repeated use\n5. **Use float32**: Set `dtype=np.float32` when precision allows\n6. **Handle errors efficiently**: Use `ignore_errors=True` for large datasets\n\n## Common Featurizers Reference\n\n**Quick reference for frequently used featurizers:**\n\n| Featurizer | Type | Dimensions | Speed | Use Case |\n|------------|------|------------|-------|----------|\n| `ecfp` | Fingerprint | 2048 | Fast | General purpose |\n| `maccs` | Fingerprint | 167 | Very fast | Scaffold similarity |\n| `desc2D` | Descriptors | 200+ | Fast | Interpretable models |\n| `mordred` | Descriptors | 1800+ | Medium | Comprehensive features |\n| `map4` | Fingerprint | 1024 | Fast | Large-scale screening |\n| `ChemBERTa-77M-MLM` | Deep learning | 768 | Slow* | Transfer learning |\n| `gin-supervised-masking` | GNN | Variable | Slow* | Graph-based models |\n\n*First run is slow; subsequent runs benefit from caching\n\n## Resources\n\nThis skill includes comprehensive reference documentation:\n\n### references\u002Fapi_reference.md\nComplete API documentation covering:\n- `molfeat.calc` - All calculator classes and parameters\n- `molfeat.trans` - Transformer classes and methods\n- `molfeat.store` - ModelStore usage\n- Common patterns and integration examples\n- Performance optimization tips\n\n**When to load:** Reference when implementing specific calculators, understanding transformer parameters, or integrating with scikit-learn\u002FPyTorch.\n\n### references\u002Favailable_featurizers.md\nComprehensive catalog of all 100+ featurizers organized by category:\n- Transformer-based language models (ChemBERTa, ChemGPT)\n- Graph neural networks (GIN, Graphormer)\n- Molecular descriptors (RDKit, Mordred)\n- Fingerprints (ECFP, MACCS, MAP4, and 15+ others)\n- Pharmacophore descriptors (CATS, Gobbi)\n- Shape descriptors (USR, ElectroShape)\n- Scaffold-based descriptors\n\n**When to load:** Reference when selecting the optimal featurizer for a specific task, exploring available options, or understanding featurizer characteristics.\n\n**Search tip:** Use grep to find specific featurizer types:\n```bash\ngrep -i \"chembert\" references\u002Favailable_featurizers.md\ngrep -i \"pharmacophore\" references\u002Favailable_featurizers.md\n```\n\n### references\u002Fexamples.md\nPractical code examples for common scenarios:\n- Installation and quick start\n- Calculator and transformer examples\n- Pretrained model usage\n- Scikit-learn and PyTorch integration\n- Virtual screening workflows\n- QSAR model building\n- Similarity searching\n- Troubleshooting and best practices\n\n**When to load:** Reference when implementing specific workflows, troubleshooting issues, or learning molfeat patterns.\n\n## Troubleshooting\n\n### Invalid Molecules\nEnable error handling to skip invalid SMILES:\n```python\ntransformer = MoleculeTransformer(\n    calc,\n    ignore_errors=True,\n    verbose=True\n)\n```\n\n### Memory Issues with Large Datasets\nProcess in chunks or use streaming approaches for datasets > 100K molecules.\n\n### Pretrained Model Dependencies\nSome models require additional packages. Install specific extras:\n```bash\nuv pip install \"molfeat[transformer]\"  # For ChemBERTa\u002FChemGPT\nuv pip install \"molfeat[dgl]\"          # For GIN models\n```\n\n### Reproducibility\nSave exact configurations and document versions:\n```python\ntransformer.to_state_yaml_file(\"config.yml\")\nimport molfeat\nprint(f\"molfeat version: {molfeat.__version__}\")\n```\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fmolfeat-docs.datamol.io\u002F\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Fdatamol-io\u002Fmolfeat\n- **PyPI Package**: https:\u002F\u002Fpypi.org\u002Fproject\u002Fmolfeat\u002F\n- **Tutorial**: https:\u002F\u002Fportal.valencelabs.com\u002Fdatamol\u002Fpost\u002Ftypes-of-featurizers-b1e8HHrbFMkbun6\n",{"name":5,"description":7},"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmolfeat",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add JSGforever\u002Fskillguard --skill molfeat",{"tool":44,"command":45},"bunx","bunx skills add JSGforever\u002Fskillguard --skill molfeat",{"tool":47,"command":48},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill molfeat","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmolfeat\u002FSKILL.md",true,[52,58,62],{"path":53,"resourceType":54,"resourceTypeDesc":55,"sizeBytes":56,"sourceHtmlUrl":57},"references\u002Fapi_reference.md","REFERENCE","参考文档",11502,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmolfeat\u002Freferences\u002Fapi_reference.md",{"path":59,"resourceType":54,"resourceTypeDesc":55,"sizeBytes":60,"sourceHtmlUrl":61},"references\u002Favailable_featurizers.md",10709,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmolfeat\u002Freferences\u002Favailable_featurizers.md",{"path":63,"resourceType":54,"resourceTypeDesc":55,"sizeBytes":64,"sourceHtmlUrl":65},"references\u002Fexamples.md",17709,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fmolfeat\u002Freferences\u002Fexamples.md",[67,99,115,142,164,185],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":78,"publishedAt":26},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. 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NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":151,"publishedAt":26},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. 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NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[148],{"categoryNo":149,"categoryName":150,"taxonomyType":76,"taxonomyTypeDesc":77},"01JBSEEDCAT000000000000009","前端开发",[131,152,139,140,153,154,155,156,157,158,122,159,160,135,137,161,138,141,162,163],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":171,"publishedAt":26},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. 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