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Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"use","prediction","when","graph neural networks","for","you","quick","synthetic","molecular","options","workflows","discovery","pytdc","networks","graph","benchmark","pytorch","neural networks","experiments","2026-11-10 10:12:30",false,"---\nname: deepchem\ndescription: Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.\ncategory: chemistry\nlicense: MIT license\nmetadata:\n    skill-author: Synthetic Sciences\nversion: 1.0.0\nauthor: Synthetic Sciences\ntags: [Drug Discovery, Molecular ML, ADMET, Graph Neural Networks]\ndependencies: [\"deepchem>=2.8.0\", \"torch>=2.0.0\", \"numpy>=1.25.0\"]\n---\n\n# DeepChem\n\n## Overview\n\nDeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Loading and processing molecular data (SMILES strings, SDF files, protein sequences)\n- Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)\n- Training models on chemical\u002Fbiological datasets\n- Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)\n- Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)\n- Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)\n- Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)\n- Predicting crystal\u002Fmaterials properties (bandgap, formation energy)\n- Analyzing protein or DNA sequences\n\n## Core Capabilities\n\n### 1. Molecular Data Loading and Processing\n\nDeepChem provides specialized loaders for various chemical data formats:\n\n```python\nimport deepchem as dc\n\n# Load CSV with SMILES\nfeaturizer = dc.feat.CircularFingerprint(radius=2, size=2048)\nloader = dc.data.CSVLoader(\n    tasks=['solubility', 'toxicity'],\n    feature_field='smiles',\n    featurizer=featurizer\n)\ndataset = loader.create_dataset('molecules.csv')\n\n# Load SDF files\nloader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer)\ndataset = loader.create_dataset('compounds.sdf')\n\n# Load protein sequences\nloader = dc.data.FASTALoader()\ndataset = loader.create_dataset('proteins.fasta')\n```\n\n**Key Loaders**:\n- `CSVLoader`: Tabular data with molecular identifiers\n- `SDFLoader`: Molecular structure files\n- `FASTALoader`: Protein\u002FDNA sequences\n- `ImageLoader`: Molecular images\n- `JsonLoader`: JSON-formatted datasets\n\n### 2. Molecular Featurization\n\nConvert molecules into numerical representations for ML models.\n\n#### Decision Tree for Featurizer Selection\n\n```\nIs the model a graph neural network?\n├─ YES → Use graph featurizers\n│   ├─ Standard GNN → MolGraphConvFeaturizer\n│   ├─ Message passing → DMPNNFeaturizer\n│   └─ Pretrained → GroverFeaturizer\n│\n└─ NO → What type of model?\n    ├─ Traditional ML (RF, XGBoost, SVM)\n    │   ├─ Fast baseline → CircularFingerprint (ECFP)\n    │   ├─ Interpretable → RDKitDescriptors\n    │   └─ Maximum coverage → MordredDescriptors\n    │\n    ├─ Deep learning (non-graph)\n    │   ├─ Dense networks → CircularFingerprint\n    │   └─ CNN → SmilesToImage\n    │\n    ├─ Sequence models (LSTM, Transformer)\n    │   └─ SmilesToSeq\n    │\n    └─ 3D structure analysis\n        └─ CoulombMatrix\n```\n\n#### Example Featurization\n\n```python\n# Fingerprints (for traditional ML)\nfp = dc.feat.CircularFingerprint(radius=2, size=2048)\n\n# Descriptors (for interpretable models)\ndesc = dc.feat.RDKitDescriptors()\n\n# Graph features (for GNNs)\ngraph_feat = dc.feat.MolGraphConvFeaturizer()\n\n# Apply featurization\nfeatures = fp.featurize(['CCO', 'c1ccccc1'])\n```\n\n**Selection Guide**:\n- **Small datasets (\u003C1K)**: CircularFingerprint or RDKitDescriptors\n- **Medium datasets (1K-100K)**: CircularFingerprint or graph featurizers\n- **Large datasets (>100K)**: Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer)\n- **Transfer learning**: Pretrained model featurizers (GroverFeaturizer)\n\nSee `references\u002Fapi_reference.md` for complete featurizer documentation.\n\n### 3. Data Splitting\n\n**Critical**: For drug discovery tasks, use `ScaffoldSplitter` to prevent data leakage from similar molecular structures appearing in both training and test sets.\n\n```python\n# Scaffold splitting (recommended for molecules)\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, valid, test = splitter.train_valid_test_split(\n    dataset,\n    frac_train=0.8,\n    frac_valid=0.1,\n    frac_test=0.1\n)\n\n# Random splitting (for non-molecular data)\nsplitter = dc.splits.RandomSplitter()\ntrain, test = splitter.train_test_split(dataset)\n\n# Stratified splitting (for imbalanced classification)\nsplitter = dc.splits.RandomStratifiedSplitter()\ntrain, test = splitter.train_test_split(dataset)\n```\n\n**Available Splitters**:\n- `ScaffoldSplitter`: Split by molecular scaffolds (prevents leakage)\n- `ButinaSplitter`: Clustering-based molecular splitting\n- `MaxMinSplitter`: Maximize diversity between sets\n- `RandomSplitter`: Random splitting\n- `RandomStratifiedSplitter`: Preserves class distributions\n\n### 4. Model Selection and Training\n\n#### Quick Model Selection Guide\n\n| Dataset Size | Task | Recommended Model | Featurizer |\n|-------------|------|-------------------|------------|\n| \u003C 1K samples | Any | SklearnModel (RandomForest) | CircularFingerprint |\n| 1K-100K | Classification\u002FRegression | GBDTModel or MultitaskRegressor | CircularFingerprint |\n| > 100K | Molecular properties | GCNModel, AttentiveFPModel, DMPNNModel | MolGraphConvFeaturizer |\n| Any (small preferred) | Transfer learning | ChemBERTa, GROVER, MolFormer | Model-specific |\n| Crystal structures | Materials properties | CGCNNModel, MEGNetModel | Structure-based |\n| Protein sequences | Protein properties | ProtBERT | Sequence-based |\n\n#### Example: Traditional ML\n```python\nfrom sklearn.ensemble import RandomForestRegressor\n\n# Wrap scikit-learn model\nsklearn_model = RandomForestRegressor(n_estimators=100)\nmodel = dc.models.SklearnModel(model=sklearn_model)\nmodel.fit(train)\n```\n\n#### Example: Deep Learning\n```python\n# Multitask regressor (for fingerprints)\nmodel = dc.models.MultitaskRegressor(\n    n_tasks=2,\n    n_features=2048,\n    layer_sizes=[1000, 500],\n    dropouts=0.25,\n    learning_rate=0.001\n)\nmodel.fit(train, nb_epoch=50)\n```\n\n#### Example: Graph Neural Networks\n```python\n# Graph Convolutional Network\nmodel = dc.models.GCNModel(\n    n_tasks=1,\n    mode='regression',\n    batch_size=128,\n    learning_rate=0.001\n)\nmodel.fit(train, nb_epoch=50)\n\n# Graph Attention Network\nmodel = dc.models.GATModel(n_tasks=1, mode='classification')\nmodel.fit(train, nb_epoch=50)\n\n# Attentive Fingerprint\nmodel = dc.models.AttentiveFPModel(n_tasks=1, mode='regression')\nmodel.fit(train, nb_epoch=50)\n```\n\n### 5. MoleculeNet Benchmarks\n\nQuick access to 30+ curated benchmark datasets with standardized train\u002Fvalid\u002Ftest splits:\n\n```python\n# Load benchmark dataset\ntasks, datasets, transformers = dc.molnet.load_tox21(\n    featurizer='GraphConv',  # or 'ECFP', 'Weave', 'Raw'\n    splitter='scaffold',     # or 'random', 'stratified'\n    reload=False\n)\ntrain, valid, test = datasets\n\n# Train and evaluate\nmodel = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')\nmodel.fit(train, nb_epoch=50)\n\nmetric = dc.metrics.Metric(dc.metrics.roc_auc_score)\ntest_score = model.evaluate(test, [metric])\n```\n\n**Common Datasets**:\n- **Classification**: `load_tox21()`, `load_bbbp()`, `load_hiv()`, `load_clintox()`\n- **Regression**: `load_delaney()`, `load_freesolv()`, `load_lipo()`\n- **Quantum properties**: `load_qm7()`, `load_qm8()`, `load_qm9()`\n- **Materials**: `load_perovskite()`, `load_bandgap()`, `load_mp_formation_energy()`\n\nSee `references\u002Fapi_reference.md` for complete dataset list.\n\n### 6. Transfer Learning\n\nLeverage pretrained models for improved performance, especially on small datasets:\n\n```python\n# ChemBERTa (BERT pretrained on 77M molecules)\nmodel = dc.models.HuggingFaceModel(\n    model='seyonec\u002FChemBERTa-zinc-base-v1',\n    task='classification',\n    n_tasks=1,\n    learning_rate=2e-5  # Lower LR for fine-tuning\n)\nmodel.fit(train, nb_epoch=10)\n\n# GROVER (graph transformer pretrained on 10M molecules)\nmodel = dc.models.GroverModel(\n    task='regression',\n    n_tasks=1\n)\nmodel.fit(train, nb_epoch=20)\n```\n\n**When to use transfer learning**:\n- Small datasets (\u003C 1000 samples)\n- Novel molecular scaffolds\n- Limited computational resources\n- Need for rapid prototyping\n\nUse the `scripts\u002Ftransfer_learning.py` script for guided transfer learning workflows.\n\n### 7. Model Evaluation\n\n```python\n# Define metrics\nclassification_metrics = [\n    dc.metrics.Metric(dc.metrics.roc_auc_score, name='ROC-AUC'),\n    dc.metrics.Metric(dc.metrics.accuracy_score, name='Accuracy'),\n    dc.metrics.Metric(dc.metrics.f1_score, name='F1')\n]\n\nregression_metrics = [\n    dc.metrics.Metric(dc.metrics.r2_score, name='R²'),\n    dc.metrics.Metric(dc.metrics.mean_absolute_error, name='MAE'),\n    dc.metrics.Metric(dc.metrics.root_mean_squared_error, name='RMSE')\n]\n\n# Evaluate\ntrain_scores = model.evaluate(train, classification_metrics)\ntest_scores = model.evaluate(test, classification_metrics)\n```\n\n### 8. Making Predictions\n\n```python\n# Predict on test set\npredictions = model.predict(test)\n\n# Predict on new molecules\nnew_smiles = ['CCO', 'c1ccccc1', 'CC(C)O']\nnew_features = featurizer.featurize(new_smiles)\nnew_dataset = dc.data.NumpyDataset(X=new_features)\n\n# Apply same transformations as training\nfor transformer in transformers:\n    new_dataset = transformer.transform(new_dataset)\n\npredictions = model.predict(new_dataset)\n```\n\n## Typical Workflows\n\n### Workflow A: Quick Benchmark Evaluation\n\nFor evaluating a model on standard benchmarks:\n\n```python\nimport deepchem as dc\n\n# 1. Load benchmark\ntasks, datasets, _ = dc.molnet.load_bbbp(\n    featurizer='GraphConv',\n    splitter='scaffold'\n)\ntrain, valid, test = datasets\n\n# 2. Train model\nmodel = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')\nmodel.fit(train, nb_epoch=50)\n\n# 3. Evaluate\nmetric = dc.metrics.Metric(dc.metrics.roc_auc_score)\ntest_score = model.evaluate(test, [metric])\nprint(f\"Test ROC-AUC: {test_score}\")\n```\n\n### Workflow B: Custom Data Prediction\n\nFor training on custom molecular datasets:\n\n```python\nimport deepchem as dc\n\n# 1. Load and featurize data\nfeaturizer = dc.feat.CircularFingerprint(radius=2, size=2048)\nloader = dc.data.CSVLoader(\n    tasks=['activity'],\n    feature_field='smiles',\n    featurizer=featurizer\n)\ndataset = loader.create_dataset('my_molecules.csv')\n\n# 2. Split data (use ScaffoldSplitter for molecules!)\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, valid, test = splitter.train_valid_test_split(dataset)\n\n# 3. Normalize (optional but recommended)\ntransformers = [dc.trans.NormalizationTransformer(\n    transform_y=True, dataset=train\n)]\nfor transformer in transformers:\n    train = transformer.transform(train)\n    valid = transformer.transform(valid)\n    test = transformer.transform(test)\n\n# 4. Train model\nmodel = dc.models.MultitaskRegressor(\n    n_tasks=1,\n    n_features=2048,\n    layer_sizes=[1000, 500],\n    dropouts=0.25\n)\nmodel.fit(train, nb_epoch=50)\n\n# 5. Evaluate\nmetric = dc.metrics.Metric(dc.metrics.r2_score)\ntest_score = model.evaluate(test, [metric])\n```\n\n### Workflow C: Transfer Learning on Small Dataset\n\nFor leveraging pretrained models:\n\n```python\nimport deepchem as dc\n\n# 1. Load data (pretrained models often need raw SMILES)\nloader = dc.data.CSVLoader(\n    tasks=['activity'],\n    feature_field='smiles',\n    featurizer=dc.feat.DummyFeaturizer()  # Model handles featurization\n)\ndataset = loader.create_dataset('small_dataset.csv')\n\n# 2. Split data\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, test = splitter.train_test_split(dataset)\n\n# 3. Load pretrained model\nmodel = dc.models.HuggingFaceModel(\n    model='seyonec\u002FChemBERTa-zinc-base-v1',\n    task='classification',\n    n_tasks=1,\n    learning_rate=2e-5\n)\n\n# 4. Fine-tune\nmodel.fit(train, nb_epoch=10)\n\n# 5. Evaluate\npredictions = model.predict(test)\n```\n\nSee `references\u002Fworkflows.md` for 8 detailed workflow examples covering molecular generation, materials science, protein analysis, and more.\n\n## Example Scripts\n\nThis skill includes three production-ready scripts in the `scripts\u002F` directory:\n\n### 1. `predict_solubility.py`\nTrain and evaluate solubility prediction models. Works with Delaney benchmark or custom CSV data.\n\n```bash\n# Use Delaney benchmark\npython scripts\u002Fpredict_solubility.py\n\n# Use custom data\npython scripts\u002Fpredict_solubility.py \\\n    --data my_data.csv \\\n    --smiles-col smiles \\\n    --target-col solubility \\\n    --predict \"CCO\" \"c1ccccc1\"\n```\n\n### 2. `graph_neural_network.py`\nTrain various graph neural network architectures on molecular data.\n\n```bash\n# Train GCN on Tox21\npython scripts\u002Fgraph_neural_network.py --model gcn --dataset tox21\n\n# Train AttentiveFP on custom data\npython scripts\u002Fgraph_neural_network.py \\\n    --model attentivefp \\\n    --data molecules.csv \\\n    --task-type regression \\\n    --targets activity \\\n    --epochs 100\n```\n\n### 3. `transfer_learning.py`\nFine-tune pretrained models (ChemBERTa, GROVER) on molecular property prediction tasks.\n\n```bash\n# Fine-tune ChemBERTa on BBBP\npython scripts\u002Ftransfer_learning.py --model chemberta --dataset bbbp\n\n# Fine-tune GROVER on custom data\npython scripts\u002Ftransfer_learning.py \\\n    --model grover \\\n    --data small_dataset.csv \\\n    --target activity \\\n    --task-type classification \\\n    --epochs 20\n```\n\n## Common Patterns and Best Practices\n\n### Pattern 1: Always Use Scaffold Splitting for Molecules\n```python\n# GOOD: Prevents data leakage\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, test = splitter.train_test_split(dataset)\n\n# BAD: Similar molecules in train and test\nsplitter = dc.splits.RandomSplitter()\ntrain, test = splitter.train_test_split(dataset)\n```\n\n### Pattern 2: Normalize Features and Targets\n```python\ntransformers = [\n    dc.trans.NormalizationTransformer(\n        transform_y=True,  # Also normalize target values\n        dataset=train\n    )\n]\nfor transformer in transformers:\n    train = transformer.transform(train)\n    test = transformer.transform(test)\n```\n\n### Pattern 3: Start Simple, Then Scale\n1. Start with Random Forest + CircularFingerprint (fast baseline)\n2. Try XGBoost\u002FLightGBM if RF works well\n3. Move to deep learning (MultitaskRegressor) if you have >5K samples\n4. Try GNNs if you have >10K samples\n5. Use transfer learning for small datasets or novel scaffolds\n\n### Pattern 4: Handle Imbalanced Data\n```python\n# Option 1: Balancing transformer\ntransformer = dc.trans.BalancingTransformer(dataset=train)\ntrain = transformer.transform(train)\n\n# Option 2: Use balanced metrics\nmetric = dc.metrics.Metric(dc.metrics.balanced_accuracy_score)\n```\n\n### Pattern 5: Avoid Memory Issues\n```python\n# Use DiskDataset for large datasets\ndataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)\n\n# Use smaller batch sizes\nmodel = dc.models.GCNModel(batch_size=32)  # Instead of 128\n```\n\n## Common Pitfalls\n\n### Issue 1: Data Leakage in Drug Discovery\n**Problem**: Using random splitting allows similar molecules in train\u002Ftest sets.\n**Solution**: Always use `ScaffoldSplitter` for molecular datasets.\n\n### Issue 2: GNN Underperforming vs Fingerprints\n**Problem**: Graph neural networks perform worse than simple fingerprints.\n**Solutions**:\n- Ensure dataset is large enough (>10K samples typically)\n- Increase training epochs (50-100)\n- Try different architectures (AttentiveFP, DMPNN instead of GCN)\n- Use pretrained models (GROVER)\n\n### Issue 3: Overfitting on Small Datasets\n**Problem**: Model memorizes training data.\n**Solutions**:\n- Use stronger regularization (increase dropout to 0.5)\n- Use simpler models (Random Forest instead of deep learning)\n- Apply transfer learning (ChemBERTa, GROVER)\n- Collect more data\n\n### Issue 4: Import Errors\n**Problem**: Module not found errors.\n**Solution**: Ensure DeepChem is installed with required dependencies:\n```bash\nuv pip install deepchem\n# For PyTorch models\nuv pip install deepchem[torch]\n# For all features\nuv pip install deepchem[all]\n```\n\n## Reference Documentation\n\nThis skill includes comprehensive reference documentation:\n\n### `references\u002Fapi_reference.md`\nComplete API documentation including:\n- All data loaders and their use cases\n- Dataset classes and when to use each\n- Complete featurizer catalog with selection guide\n- Model catalog organized by category (50+ models)\n- MoleculeNet dataset descriptions\n- Metrics and evaluation functions\n- Common code patterns\n\n**When to reference**: Search this file when you need specific API details, parameter names, or want to explore available options.\n\n### `references\u002Fworkflows.md`\nEight detailed end-to-end workflows:\n1. Molecular property prediction from SMILES\n2. Using MoleculeNet benchmarks\n3. Hyperparameter optimization\n4. Transfer learning with pretrained models\n5. Molecular generation with GANs\n6. Materials property prediction\n7. Protein sequence analysis\n8. Custom model integration\n\n**When to reference**: Use these workflows as templates for implementing complete solutions.\n\n## Installation Notes\n\nBasic installation:\n```bash\nuv pip install deepchem\n```\n\nFor PyTorch models (GCN, GAT, etc.):\n```bash\nuv pip install deepchem[torch]\n```\n\nFor all features:\n```bash\nuv pip install deepchem[all]\n```\n\nIf import errors occur, the user may need specific dependencies. Check the DeepChem documentation for detailed installation instructions.\n\n## Additional Resources\n\n- Official documentation: https:\u002F\u002Fdeepchem.readthedocs.io\u002F\n- GitHub repository: https:\u002F\u002Fgithub.com\u002Fdeepchem\u002Fdeepchem\n- Tutorials: https:\u002F\u002Fdeepchem.readthedocs.io\u002Fen\u002Flatest\u002Fget_started\u002Ftutorials.html\n- Paper: \"MoleculeNet: A Benchmark for Molecular Machine Learning\"\n\n","# DeepChem\n\n## Overview\n\nDeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Loading and processing molecular data (SMILES strings, SDF files, protein sequences)\n- Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)\n- Training models on chemical\u002Fbiological datasets\n- Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)\n- Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)\n- Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)\n- Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)\n- Predicting crystal\u002Fmaterials properties (bandgap, formation energy)\n- Analyzing protein or DNA sequences\n\n## Core Capabilities\n\n### 1. Molecular Data Loading and Processing\n\nDeepChem provides specialized loaders for various chemical data formats:\n\n```python\nimport deepchem as dc\n\n# Load CSV with SMILES\nfeaturizer = dc.feat.CircularFingerprint(radius=2, size=2048)\nloader = dc.data.CSVLoader(\n    tasks=['solubility', 'toxicity'],\n    feature_field='smiles',\n    featurizer=featurizer\n)\ndataset = loader.create_dataset('molecules.csv')\n\n# Load SDF files\nloader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer)\ndataset = loader.create_dataset('compounds.sdf')\n\n# Load protein sequences\nloader = dc.data.FASTALoader()\ndataset = loader.create_dataset('proteins.fasta')\n```\n\n**Key Loaders**:\n- `CSVLoader`: Tabular data with molecular identifiers\n- `SDFLoader`: Molecular structure files\n- `FASTALoader`: Protein\u002FDNA sequences\n- `ImageLoader`: Molecular images\n- `JsonLoader`: JSON-formatted datasets\n\n### 2. Molecular Featurization\n\nConvert molecules into numerical representations for ML models.\n\n#### Decision Tree for Featurizer Selection\n\n```\nIs the model a graph neural network?\n├─ YES → Use graph featurizers\n│   ├─ Standard GNN → MolGraphConvFeaturizer\n│   ├─ Message passing → DMPNNFeaturizer\n│   └─ Pretrained → GroverFeaturizer\n│\n└─ NO → What type of model?\n    ├─ Traditional ML (RF, XGBoost, SVM)\n    │   ├─ Fast baseline → CircularFingerprint (ECFP)\n    │   ├─ Interpretable → RDKitDescriptors\n    │   └─ Maximum coverage → MordredDescriptors\n    │\n    ├─ Deep learning (non-graph)\n    │   ├─ Dense networks → CircularFingerprint\n    │   └─ CNN → SmilesToImage\n    │\n    ├─ Sequence models (LSTM, Transformer)\n    │   └─ SmilesToSeq\n    │\n    └─ 3D structure analysis\n        └─ CoulombMatrix\n```\n\n#### Example Featurization\n\n```python\n# Fingerprints (for traditional ML)\nfp = dc.feat.CircularFingerprint(radius=2, size=2048)\n\n# Descriptors (for interpretable models)\ndesc = dc.feat.RDKitDescriptors()\n\n# Graph features (for GNNs)\ngraph_feat = dc.feat.MolGraphConvFeaturizer()\n\n# Apply featurization\nfeatures = fp.featurize(['CCO', 'c1ccccc1'])\n```\n\n**Selection Guide**:\n- **Small datasets (\u003C1K)**: CircularFingerprint or RDKitDescriptors\n- **Medium datasets (1K-100K)**: CircularFingerprint or graph featurizers\n- **Large datasets (>100K)**: Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer)\n- **Transfer learning**: Pretrained model featurizers (GroverFeaturizer)\n\nSee `references\u002Fapi_reference.md` for complete featurizer documentation.\n\n### 3. Data Splitting\n\n**Critical**: For drug discovery tasks, use `ScaffoldSplitter` to prevent data leakage from similar molecular structures appearing in both training and test sets.\n\n```python\n# Scaffold splitting (recommended for molecules)\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, valid, test = splitter.train_valid_test_split(\n    dataset,\n    frac_train=0.8,\n    frac_valid=0.1,\n    frac_test=0.1\n)\n\n# Random splitting (for non-molecular data)\nsplitter = dc.splits.RandomSplitter()\ntrain, test = splitter.train_test_split(dataset)\n\n# Stratified splitting (for imbalanced classification)\nsplitter = dc.splits.RandomStratifiedSplitter()\ntrain, test = splitter.train_test_split(dataset)\n```\n\n**Available Splitters**:\n- `ScaffoldSplitter`: Split by molecular scaffolds (prevents leakage)\n- `ButinaSplitter`: Clustering-based molecular splitting\n- `MaxMinSplitter`: Maximize diversity between sets\n- `RandomSplitter`: Random splitting\n- `RandomStratifiedSplitter`: Preserves class distributions\n\n### 4. Model Selection and Training\n\n#### Quick Model Selection Guide\n\n| Dataset Size | Task | Recommended Model | Featurizer |\n|-------------|------|-------------------|------------|\n| \u003C 1K samples | Any | SklearnModel (RandomForest) | CircularFingerprint |\n| 1K-100K | Classification\u002FRegression | GBDTModel or MultitaskRegressor | CircularFingerprint |\n| > 100K | Molecular properties | GCNModel, AttentiveFPModel, DMPNNModel | MolGraphConvFeaturizer |\n| Any (small preferred) | Transfer learning | ChemBERTa, GROVER, MolFormer | Model-specific |\n| Crystal structures | Materials properties | CGCNNModel, MEGNetModel | Structure-based |\n| Protein sequences | Protein properties | ProtBERT | Sequence-based |\n\n#### Example: Traditional ML\n```python\nfrom sklearn.ensemble import RandomForestRegressor\n\n# Wrap scikit-learn model\nsklearn_model = RandomForestRegressor(n_estimators=100)\nmodel = dc.models.SklearnModel(model=sklearn_model)\nmodel.fit(train)\n```\n\n#### Example: Deep Learning\n```python\n# Multitask regressor (for fingerprints)\nmodel = dc.models.MultitaskRegressor(\n    n_tasks=2,\n    n_features=2048,\n    layer_sizes=[1000, 500],\n    dropouts=0.25,\n    learning_rate=0.001\n)\nmodel.fit(train, nb_epoch=50)\n```\n\n#### Example: Graph Neural Networks\n```python\n# Graph Convolutional Network\nmodel = dc.models.GCNModel(\n    n_tasks=1,\n    mode='regression',\n    batch_size=128,\n    learning_rate=0.001\n)\nmodel.fit(train, nb_epoch=50)\n\n# Graph Attention Network\nmodel = dc.models.GATModel(n_tasks=1, mode='classification')\nmodel.fit(train, nb_epoch=50)\n\n# Attentive Fingerprint\nmodel = dc.models.AttentiveFPModel(n_tasks=1, mode='regression')\nmodel.fit(train, nb_epoch=50)\n```\n\n### 5. MoleculeNet Benchmarks\n\nQuick access to 30+ curated benchmark datasets with standardized train\u002Fvalid\u002Ftest splits:\n\n```python\n# Load benchmark dataset\ntasks, datasets, transformers = dc.molnet.load_tox21(\n    featurizer='GraphConv',  # or 'ECFP', 'Weave', 'Raw'\n    splitter='scaffold',     # or 'random', 'stratified'\n    reload=False\n)\ntrain, valid, test = datasets\n\n# Train and evaluate\nmodel = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')\nmodel.fit(train, nb_epoch=50)\n\nmetric = dc.metrics.Metric(dc.metrics.roc_auc_score)\ntest_score = model.evaluate(test, [metric])\n```\n\n**Common Datasets**:\n- **Classification**: `load_tox21()`, `load_bbbp()`, `load_hiv()`, `load_clintox()`\n- **Regression**: `load_delaney()`, `load_freesolv()`, `load_lipo()`\n- **Quantum properties**: `load_qm7()`, `load_qm8()`, `load_qm9()`\n- **Materials**: `load_perovskite()`, `load_bandgap()`, `load_mp_formation_energy()`\n\nSee `references\u002Fapi_reference.md` for complete dataset list.\n\n### 6. Transfer Learning\n\nLeverage pretrained models for improved performance, especially on small datasets:\n\n```python\n# ChemBERTa (BERT pretrained on 77M molecules)\nmodel = dc.models.HuggingFaceModel(\n    model='seyonec\u002FChemBERTa-zinc-base-v1',\n    task='classification',\n    n_tasks=1,\n    learning_rate=2e-5  # Lower LR for fine-tuning\n)\nmodel.fit(train, nb_epoch=10)\n\n# GROVER (graph transformer pretrained on 10M molecules)\nmodel = dc.models.GroverModel(\n    task='regression',\n    n_tasks=1\n)\nmodel.fit(train, nb_epoch=20)\n```\n\n**When to use transfer learning**:\n- Small datasets (\u003C 1000 samples)\n- Novel molecular scaffolds\n- Limited computational resources\n- Need for rapid prototyping\n\nUse the `scripts\u002Ftransfer_learning.py` script for guided transfer learning workflows.\n\n### 7. Model Evaluation\n\n```python\n# Define metrics\nclassification_metrics = [\n    dc.metrics.Metric(dc.metrics.roc_auc_score, name='ROC-AUC'),\n    dc.metrics.Metric(dc.metrics.accuracy_score, name='Accuracy'),\n    dc.metrics.Metric(dc.metrics.f1_score, name='F1')\n]\n\nregression_metrics = [\n    dc.metrics.Metric(dc.metrics.r2_score, name='R²'),\n    dc.metrics.Metric(dc.metrics.mean_absolute_error, name='MAE'),\n    dc.metrics.Metric(dc.metrics.root_mean_squared_error, name='RMSE')\n]\n\n# Evaluate\ntrain_scores = model.evaluate(train, classification_metrics)\ntest_scores = model.evaluate(test, classification_metrics)\n```\n\n### 8. Making Predictions\n\n```python\n# Predict on test set\npredictions = model.predict(test)\n\n# Predict on new molecules\nnew_smiles = ['CCO', 'c1ccccc1', 'CC(C)O']\nnew_features = featurizer.featurize(new_smiles)\nnew_dataset = dc.data.NumpyDataset(X=new_features)\n\n# Apply same transformations as training\nfor transformer in transformers:\n    new_dataset = transformer.transform(new_dataset)\n\npredictions = model.predict(new_dataset)\n```\n\n## Typical Workflows\n\n### Workflow A: Quick Benchmark Evaluation\n\nFor evaluating a model on standard benchmarks:\n\n```python\nimport deepchem as dc\n\n# 1. Load benchmark\ntasks, datasets, _ = dc.molnet.load_bbbp(\n    featurizer='GraphConv',\n    splitter='scaffold'\n)\ntrain, valid, test = datasets\n\n# 2. Train model\nmodel = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')\nmodel.fit(train, nb_epoch=50)\n\n# 3. Evaluate\nmetric = dc.metrics.Metric(dc.metrics.roc_auc_score)\ntest_score = model.evaluate(test, [metric])\nprint(f\"Test ROC-AUC: {test_score}\")\n```\n\n### Workflow B: Custom Data Prediction\n\nFor training on custom molecular datasets:\n\n```python\nimport deepchem as dc\n\n# 1. Load and featurize data\nfeaturizer = dc.feat.CircularFingerprint(radius=2, size=2048)\nloader = dc.data.CSVLoader(\n    tasks=['activity'],\n    feature_field='smiles',\n    featurizer=featurizer\n)\ndataset = loader.create_dataset('my_molecules.csv')\n\n# 2. Split data (use ScaffoldSplitter for molecules!)\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, valid, test = splitter.train_valid_test_split(dataset)\n\n# 3. Normalize (optional but recommended)\ntransformers = [dc.trans.NormalizationTransformer(\n    transform_y=True, dataset=train\n)]\nfor transformer in transformers:\n    train = transformer.transform(train)\n    valid = transformer.transform(valid)\n    test = transformer.transform(test)\n\n# 4. Train model\nmodel = dc.models.MultitaskRegressor(\n    n_tasks=1,\n    n_features=2048,\n    layer_sizes=[1000, 500],\n    dropouts=0.25\n)\nmodel.fit(train, nb_epoch=50)\n\n# 5. Evaluate\nmetric = dc.metrics.Metric(dc.metrics.r2_score)\ntest_score = model.evaluate(test, [metric])\n```\n\n### Workflow C: Transfer Learning on Small Dataset\n\nFor leveraging pretrained models:\n\n```python\nimport deepchem as dc\n\n# 1. Load data (pretrained models often need raw SMILES)\nloader = dc.data.CSVLoader(\n    tasks=['activity'],\n    feature_field='smiles',\n    featurizer=dc.feat.DummyFeaturizer()  # Model handles featurization\n)\ndataset = loader.create_dataset('small_dataset.csv')\n\n# 2. Split data\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, test = splitter.train_test_split(dataset)\n\n# 3. Load pretrained model\nmodel = dc.models.HuggingFaceModel(\n    model='seyonec\u002FChemBERTa-zinc-base-v1',\n    task='classification',\n    n_tasks=1,\n    learning_rate=2e-5\n)\n\n# 4. Fine-tune\nmodel.fit(train, nb_epoch=10)\n\n# 5. Evaluate\npredictions = model.predict(test)\n```\n\nSee `references\u002Fworkflows.md` for 8 detailed workflow examples covering molecular generation, materials science, protein analysis, and more.\n\n## Example Scripts\n\nThis skill includes three production-ready scripts in the `scripts\u002F` directory:\n\n### 1. `predict_solubility.py`\nTrain and evaluate solubility prediction models. Works with Delaney benchmark or custom CSV data.\n\n```bash\n# Use Delaney benchmark\npython scripts\u002Fpredict_solubility.py\n\n# Use custom data\npython scripts\u002Fpredict_solubility.py \\\n    --data my_data.csv \\\n    --smiles-col smiles \\\n    --target-col solubility \\\n    --predict \"CCO\" \"c1ccccc1\"\n```\n\n### 2. `graph_neural_network.py`\nTrain various graph neural network architectures on molecular data.\n\n```bash\n# Train GCN on Tox21\npython scripts\u002Fgraph_neural_network.py --model gcn --dataset tox21\n\n# Train AttentiveFP on custom data\npython scripts\u002Fgraph_neural_network.py \\\n    --model attentivefp \\\n    --data molecules.csv \\\n    --task-type regression \\\n    --targets activity \\\n    --epochs 100\n```\n\n### 3. `transfer_learning.py`\nFine-tune pretrained models (ChemBERTa, GROVER) on molecular property prediction tasks.\n\n```bash\n# Fine-tune ChemBERTa on BBBP\npython scripts\u002Ftransfer_learning.py --model chemberta --dataset bbbp\n\n# Fine-tune GROVER on custom data\npython scripts\u002Ftransfer_learning.py \\\n    --model grover \\\n    --data small_dataset.csv \\\n    --target activity \\\n    --task-type classification \\\n    --epochs 20\n```\n\n## Common Patterns and Best Practices\n\n### Pattern 1: Always Use Scaffold Splitting for Molecules\n```python\n# GOOD: Prevents data leakage\nsplitter = dc.splits.ScaffoldSplitter()\ntrain, test = splitter.train_test_split(dataset)\n\n# BAD: Similar molecules in train and test\nsplitter = dc.splits.RandomSplitter()\ntrain, test = splitter.train_test_split(dataset)\n```\n\n### Pattern 2: Normalize Features and Targets\n```python\ntransformers = [\n    dc.trans.NormalizationTransformer(\n        transform_y=True,  # Also normalize target values\n        dataset=train\n    )\n]\nfor transformer in transformers:\n    train = transformer.transform(train)\n    test = transformer.transform(test)\n```\n\n### Pattern 3: Start Simple, Then Scale\n1. Start with Random Forest + CircularFingerprint (fast baseline)\n2. Try XGBoost\u002FLightGBM if RF works well\n3. Move to deep learning (MultitaskRegressor) if you have >5K samples\n4. Try GNNs if you have >10K samples\n5. Use transfer learning for small datasets or novel scaffolds\n\n### Pattern 4: Handle Imbalanced Data\n```python\n# Option 1: Balancing transformer\ntransformer = dc.trans.BalancingTransformer(dataset=train)\ntrain = transformer.transform(train)\n\n# Option 2: Use balanced metrics\nmetric = dc.metrics.Metric(dc.metrics.balanced_accuracy_score)\n```\n\n### Pattern 5: Avoid Memory Issues\n```python\n# Use DiskDataset for large datasets\ndataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)\n\n# Use smaller batch sizes\nmodel = dc.models.GCNModel(batch_size=32)  # Instead of 128\n```\n\n## Common Pitfalls\n\n### Issue 1: Data Leakage in Drug Discovery\n**Problem**: Using random splitting allows similar molecules in train\u002Ftest sets.\n**Solution**: Always use `ScaffoldSplitter` for molecular datasets.\n\n### Issue 2: GNN Underperforming vs Fingerprints\n**Problem**: Graph neural networks perform worse than simple fingerprints.\n**Solutions**:\n- Ensure dataset is large enough (>10K samples typically)\n- Increase training epochs (50-100)\n- Try different architectures (AttentiveFP, DMPNN instead of GCN)\n- Use pretrained models (GROVER)\n\n### Issue 3: Overfitting on Small Datasets\n**Problem**: Model memorizes training data.\n**Solutions**:\n- Use stronger regularization (increase dropout to 0.5)\n- Use simpler models (Random Forest instead of deep learning)\n- Apply transfer learning (ChemBERTa, GROVER)\n- Collect more data\n\n### Issue 4: Import Errors\n**Problem**: Module not found errors.\n**Solution**: Ensure DeepChem is installed with required dependencies:\n```bash\nuv pip install deepchem\n# For PyTorch models\nuv pip install deepchem[torch]\n# For all features\nuv pip install deepchem[all]\n```\n\n## Reference Documentation\n\nThis skill includes comprehensive reference documentation:\n\n### `references\u002Fapi_reference.md`\nComplete API documentation including:\n- All data loaders and their use cases\n- Dataset classes and when to use each\n- Complete featurizer catalog with selection guide\n- Model catalog organized by category (50+ models)\n- MoleculeNet dataset descriptions\n- Metrics and evaluation functions\n- Common code patterns\n\n**When to reference**: Search this file when you need specific API details, parameter names, or want to explore available options.\n\n### `references\u002Fworkflows.md`\nEight detailed end-to-end workflows:\n1. Molecular property prediction from SMILES\n2. Using MoleculeNet benchmarks\n3. Hyperparameter optimization\n4. Transfer learning with pretrained models\n5. Molecular generation with GANs\n6. Materials property prediction\n7. Protein sequence analysis\n8. Custom model integration\n\n**When to reference**: Use these workflows as templates for implementing complete solutions.\n\n## Installation Notes\n\nBasic installation:\n```bash\nuv pip install deepchem\n```\n\nFor PyTorch models (GCN, GAT, etc.):\n```bash\nuv pip install deepchem[torch]\n```\n\nFor all features:\n```bash\nuv pip install deepchem[all]\n```\n\nIf import errors occur, the user may need specific dependencies. Check the DeepChem documentation for detailed installation instructions.\n\n## Additional Resources\n\n- Official documentation: https:\u002F\u002Fdeepchem.readthedocs.io\u002F\n- GitHub repository: https:\u002F\u002Fgithub.com\u002Fdeepchem\u002Fdeepchem\n- Tutorials: https:\u002F\u002Fdeepchem.readthedocs.io\u002Fen\u002Flatest\u002Fget_started\u002Ftutorials.html\n- Paper: \"MoleculeNet: A Benchmark for Molecular Machine Learning\"\n\n",{"name":5,"tags":43,"author":48,"license":49,"version":50,"category":51,"metadata":52,"description":7,"dependencies":53},[44,45,46,47],"Drug Discovery","Molecular ML","ADMET","Graph Neural Networks","Synthetic Sciences","MIT license","1.0.0","chemistry",{"skill-author":48},[54,55,56],"deepchem>=2.8.0","torch>=2.0.0","numpy>=1.25.0","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add fadrienne\u002FMyGitHub --skill deepchem",{"tool":69,"command":70},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill deepchem",{"tool":72,"command":73},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill deepchem","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002FSKILL.md",true,[77,83,87,93,97],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fapi_reference.md","REFERENCE","参考文档",11597,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002Freferences\u002Fapi_reference.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fworkflows.md",11800,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002Freferences\u002Fworkflows.md",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Fgraph_neural_network.py","SCRIPT","脚本",9742,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002Fscripts\u002Fgraph_neural_network.py",{"path":94,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":95,"sourceHtmlUrl":96},"scripts\u002Fpredict_solubility.py",6546,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002Fscripts\u002Fpredict_solubility.py",{"path":98,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Ftransfer_learning.py",11353,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fchemistry\u002Fdeepchem\u002Fscripts\u002Ftransfer_learning.py",[102,133,157,191,217,244],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":113,"publishedAt":38},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[108],{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[114,115,116,117,118,119,120,121,21,122,123,124,125,126,127,128,129,130,131,132],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":142,"publishedAt":38},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[139],{"categoryNo":140,"categoryName":141,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000008","测试与质量",[124,143,114,144,145,146,21,24,147,148,23,149,19,150,151,152,153,154,155,156],"statistical analysis","academic research","implementing","selection","reporting","data","tests","with","test","research","results","statsmodels","models","help",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":172,"publishedAt":38},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[163,166,167],{"categoryNo":164,"categoryName":165,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},{"categoryNo":168,"categoryName":169,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[173,174,175,176,177,178,179,180,181,182,122,183,184,185,186,187,188,189,147,190],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":201,"publishedAt":38},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[197,200],{"categoryNo":198,"categoryName":199,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},[202,174,203,204,205,25,206,207,208,181,21,209,210,211,212,213,214,215,23,216],"visualization","scientific visualization","error","ready","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":221,"categories":222,"keywords":229,"publishedAt":38},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[223,226],{"categoryNo":224,"categoryName":225,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":227,"categoryName":228,"taxonomyType":170,"taxonomyTypeDesc":171},"01JBSEEDCAT000000000000030","开发工程师",[230,231,152,232,233,234,235,121,236,21,122,124,237,238,239,240,241,242,189,243],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":245,"name":246,"nameZh":6,"description":247,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":248,"licenseFileUrl":6,"sourceHtmlUrl":249,"categories":250,"keywords":257,"publishedAt":38},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[251,254,255,256],{"categoryNo":252,"categoryName":253,"taxonomyType":111,"taxonomyTypeDesc":112},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":164,"categoryName":165,"taxonomyType":111,"taxonomyTypeDesc":112},{"categoryNo":109,"categoryName":110,"taxonomyType":111,"taxonomyTypeDesc":112},{"categoryNo":168,"categoryName":169,"taxonomyType":170,"taxonomyTypeDesc":171},[173,258,259,260,261,262,202,263,264,21,122,124,265,266,267,268,269,270,174,189],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]