Add more scientific skills

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+---
+name: pytdc
+description: Comprehensive toolkit for therapeutic science and drug discovery using PyTDC (Therapeutics Data Commons). Use this skill when working with drug discovery datasets, ADME/toxicity prediction, drug-target interactions, molecular generation, retrosynthesis, or benchmark evaluations. Applies to tasks involving therapeutic machine learning, pharmacological property prediction, or accessing curated drug discovery datasets.
+---
+
+# PyTDC (Therapeutics Data Commons)
+
+## Overview
+
+PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery and development. It offers curated datasets spanning the entire therapeutics pipeline, from target discovery through clinical development, with standardized evaluation metrics and meaningful data splits.
+
+The platform organizes therapeutic tasks into three major categories: single-instance prediction for properties of individual biomedical entities, multi-instance prediction for relationships between multiple entities, and generation for creating new therapeutic molecules.
+
+## Installation & Setup
+
+Install PyTDC using pip:
+
+```bash
+pip install PyTDC
+```
+
+To upgrade to the latest version:
+
+```bash
+pip install PyTDC --upgrade
+```
+
+Core dependencies (automatically installed):
+- numpy, pandas, tqdm, seaborn, scikit_learn, fuzzywuzzy
+
+Additional packages are installed automatically as needed for specific features.
+
+## Quick Start
+
+The basic pattern for accessing any TDC dataset follows this structure:
+
+```python
+from tdc.<problem> import <Task>
+data = <Task>(name='<Dataset>')
+split = data.get_split(method='scaffold', seed=1, frac=[0.7, 0.1, 0.2])
+df = data.get_data(format='df')
+```
+
+Where:
+- `<problem>`: One of `single_pred`, `multi_pred`, or `generation`
+- `<Task>`: Specific task category (e.g., ADME, DTI, MolGen)
+- `<Dataset>`: Dataset name within that task
+
+**Example - Loading ADME data:**
+
+```python
+from tdc.single_pred import ADME
+data = ADME(name='Caco2_Wang')
+split = data.get_split(method='scaffold')
+# Returns dict with 'train', 'valid', 'test' DataFrames
+```
+
+## Single-Instance Prediction Tasks
+
+Single-instance prediction involves forecasting properties of individual biomedical entities (molecules, proteins, etc.).
+
+### Available Task Categories
+
+#### 1. ADME (Absorption, Distribution, Metabolism, Excretion)
+
+Predict pharmacokinetic properties of drug molecules.
+
+```python
+from tdc.single_pred import ADME
+data = ADME(name='Caco2_Wang')  # Intestinal permeability
+# Other datasets: HIA_Hou, Bioavailability_Ma, Lipophilicity_AstraZeneca, etc.
+```
+
+**Common ADME datasets:**
+- Caco2 - Intestinal permeability
+- HIA - Human intestinal absorption
+- Bioavailability - Oral bioavailability
+- Lipophilicity - Octanol-water partition coefficient
+- Solubility - Aqueous solubility
+- BBB - Blood-brain barrier penetration
+- CYP - Cytochrome P450 metabolism
+
+#### 2. Toxicity (Tox)
+
+Predict toxicity and adverse effects of compounds.
+
+```python
+from tdc.single_pred import Tox
+data = Tox(name='hERG')  # Cardiotoxicity
+# Other datasets: AMES, DILI, Carcinogens_Lagunin, etc.
+```
+
+**Common toxicity datasets:**
+- hERG - Cardiac toxicity
+- AMES - Mutagenicity
+- DILI - Drug-induced liver injury
+- Carcinogens - Carcinogenicity
+- ClinTox - Clinical trial toxicity
+
+#### 3. HTS (High-Throughput Screening)
+
+Bioactivity predictions from screening data.
+
+```python
+from tdc.single_pred import HTS
+data = HTS(name='SARSCoV2_Vitro_Touret')
+```
+
+#### 4. QM (Quantum Mechanics)
+
+Quantum mechanical properties of molecules.
+
+```python
+from tdc.single_pred import QM
+data = QM(name='QM7')
+```
+
+#### 5. Other Single Prediction Tasks
+
+- **Yields**: Chemical reaction yield prediction
+- **Epitope**: Epitope prediction for biologics
+- **Develop**: Development-stage predictions
+- **CRISPROutcome**: Gene editing outcome prediction
+
+### Data Format
+
+Single prediction datasets typically return DataFrames with columns:
+- `Drug_ID` or `Compound_ID`: Unique identifier
+- `Drug` or `X`: SMILES string or molecular representation
+- `Y`: Target label (continuous or binary)
+
+## Multi-Instance Prediction Tasks
+
+Multi-instance prediction involves forecasting properties of interactions between multiple biomedical entities.
+
+### Available Task Categories
+
+#### 1. DTI (Drug-Target Interaction)
+
+Predict binding affinity between drugs and protein targets.
+
+```python
+from tdc.multi_pred import DTI
+data = DTI(name='BindingDB_Kd')
+split = data.get_split()
+```
+
+**Available datasets:**
+- BindingDB_Kd - Dissociation constant (52,284 pairs)
+- BindingDB_IC50 - Half-maximal inhibitory concentration (991,486 pairs)
+- BindingDB_Ki - Inhibition constant (375,032 pairs)
+- DAVIS, KIBA - Kinase binding datasets
+
+**Data format:** Drug_ID, Target_ID, Drug (SMILES), Target (sequence), Y (binding affinity)
+
+#### 2. DDI (Drug-Drug Interaction)
+
+Predict interactions between drug pairs.
+
+```python
+from tdc.multi_pred import DDI
+data = DDI(name='DrugBank')
+split = data.get_split()
+```
+
+Multi-class classification task predicting interaction types. Dataset contains 191,808 DDI pairs with 1,706 drugs.
+
+#### 3. PPI (Protein-Protein Interaction)
+
+Predict protein-protein interactions.
+
+```python
+from tdc.multi_pred import PPI
+data = PPI(name='HuRI')
+```
+
+#### 4. Other Multi-Prediction Tasks
+
+- **GDA**: Gene-disease associations
+- **DrugRes**: Drug resistance prediction
+- **DrugSyn**: Drug synergy prediction
+- **PeptideMHC**: Peptide-MHC binding
+- **AntibodyAff**: Antibody affinity prediction
+- **MTI**: miRNA-target interactions
+- **Catalyst**: Catalyst prediction
+- **TrialOutcome**: Clinical trial outcome prediction
+
+## Generation Tasks
+
+Generation tasks involve creating novel biomedical entities with desired properties.
+
+### 1. Molecular Generation (MolGen)
+
+Generate diverse, novel molecules with desirable chemical properties.
+
+```python
+from tdc.generation import MolGen
+data = MolGen(name='ChEMBL_V29')
+split = data.get_split()
+```
+
+Use with oracles to optimize for specific properties:
+
+```python
+from tdc import Oracle
+oracle = Oracle(name='GSK3B')
+score = oracle('CC(C)Cc1ccc(cc1)C(C)C(O)=O')  # Evaluate SMILES
+```
+
+See `references/oracles.md` for all available oracle functions.
+
+### 2. Retrosynthesis (RetroSyn)
+
+Predict reactants needed to synthesize a target molecule.
+
+```python
+from tdc.generation import RetroSyn
+data = RetroSyn(name='USPTO')
+split = data.get_split()
+```
+
+Dataset contains 1,939,253 reactions from USPTO database.
+
+### 3. Paired Molecule Generation
+
+Generate molecule pairs (e.g., prodrug-drug pairs).
+
+```python
+from tdc.generation import PairMolGen
+data = PairMolGen(name='Prodrug')
+```
+
+For detailed oracle documentation and molecular generation workflows, refer to `references/oracles.md` and `scripts/molecular_generation.py`.
+
+## Benchmark Groups
+
+Benchmark groups provide curated collections of related datasets for systematic model evaluation.
+
+### ADMET Benchmark Group
+
+```python
+from tdc.benchmark_group import admet_group
+group = admet_group(path='data/')
+
+# Get benchmark datasets
+benchmark = group.get('Caco2_Wang')
+predictions = {}
+
+for seed in [1, 2, 3, 4, 5]:
+    train, valid = benchmark['train'], benchmark['valid']
+    # Train model here
+    predictions[seed] = model.predict(benchmark['test'])
+
+# Evaluate with required 5 seeds
+results = group.evaluate(predictions)
+```
+
+**ADMET Group includes 22 datasets** covering absorption, distribution, metabolism, excretion, and toxicity.
+
+### Other Benchmark Groups
+
+Available benchmark groups include collections for:
+- ADMET properties
+- Drug-target interactions
+- Drug combination prediction
+- And more specialized therapeutic tasks
+
+For benchmark evaluation workflows, see `scripts/benchmark_evaluation.py`.
+
+## Data Functions
+
+TDC provides comprehensive data processing utilities organized into four categories.
+
+### 1. Dataset Splits
+
+Retrieve train/validation/test partitions with various strategies:
+
+```python
+# Scaffold split (default for most tasks)
+split = data.get_split(method='scaffold', seed=1, frac=[0.7, 0.1, 0.2])
+
+# Random split
+split = data.get_split(method='random', seed=42, frac=[0.8, 0.1, 0.1])
+
+# Cold split (for DTI/DDI tasks)
+split = data.get_split(method='cold_drug', seed=1)  # Unseen drugs in test
+split = data.get_split(method='cold_target', seed=1)  # Unseen targets in test
+```
+
+**Available split strategies:**
+- `random`: Random shuffling
+- `scaffold`: Scaffold-based (for chemical diversity)
+- `cold_drug`, `cold_target`, `cold_drug_target`: For DTI tasks
+- `temporal`: Time-based splits for temporal datasets
+
+### 2. Model Evaluation
+
+Use standardized metrics for evaluation:
+
+```python
+from tdc import Evaluator
+
+# For binary classification
+evaluator = Evaluator(name='ROC-AUC')
+score = evaluator(y_true, y_pred)
+
+# For regression
+evaluator = Evaluator(name='RMSE')
+score = evaluator(y_true, y_pred)
+```
+
+**Available metrics:** ROC-AUC, PR-AUC, F1, Accuracy, RMSE, MAE, R2, Spearman, Pearson, and more.
+
+### 3. Data Processing
+
+TDC provides 11 key processing utilities:
+
+```python
+from tdc.chem_utils import MolConvert
+
+# Molecule format conversion
+converter = MolConvert(src='SMILES', dst='PyG')
+pyg_graph = converter('CC(C)Cc1ccc(cc1)C(C)C(O)=O')
+```
+
+**Processing utilities include:**
+- Molecule format conversion (SMILES, SELFIES, PyG, DGL, ECFP, etc.)
+- Molecule filters (PAINS, drug-likeness)
+- Label binarization and unit conversion
+- Data balancing (over/under-sampling)
+- Negative sampling for pair data
+- Graph transformation
+- Entity retrieval (CID to SMILES, UniProt to sequence)
+
+For comprehensive utilities documentation, see `references/utilities.md`.
+
+### 4. Molecule Generation Oracles
+
+TDC provides 17+ oracle functions for molecular optimization:
+
+```python
+from tdc import Oracle
+
+# Single oracle
+oracle = Oracle(name='DRD2')
+score = oracle('CC(C)Cc1ccc(cc1)C(C)C(O)=O')
+
+# Multiple oracles
+oracle = Oracle(name='JNK3')
+scores = oracle(['SMILES1', 'SMILES2', 'SMILES3'])
+```
+
+For complete oracle documentation, see `references/oracles.md`.
+
+## Advanced Features
+
+### Retrieve Available Datasets
+
+```python
+from tdc.utils import retrieve_dataset_names
+
+# Get all ADME datasets
+adme_datasets = retrieve_dataset_names('ADME')
+
+# Get all DTI datasets
+dti_datasets = retrieve_dataset_names('DTI')
+```
+
+### Label Transformations
+
+```python
+# Get label mapping
+label_map = data.get_label_map(name='DrugBank')
+
+# Convert labels
+from tdc.chem_utils import label_transform
+transformed = label_transform(y, from_unit='nM', to_unit='p')
+```
+
+### Database Queries
+
+```python
+from tdc.utils import cid2smiles, uniprot2seq
+
+# Convert PubChem CID to SMILES
+smiles = cid2smiles(2244)
+
+# Convert UniProt ID to amino acid sequence
+sequence = uniprot2seq('P12345')
+```
+
+## Common Workflows
+
+### Workflow 1: Train a Single Prediction Model
+
+See `scripts/load_and_split_data.py` for a complete example:
+
+```python
+from tdc.single_pred import ADME
+from tdc import Evaluator
+
+# Load data
+data = ADME(name='Caco2_Wang')
+split = data.get_split(method='scaffold', seed=42)
+
+train, valid, test = split['train'], split['valid'], split['test']
+
+# Train model (user implements)
+# model.fit(train['Drug'], train['Y'])
+
+# Evaluate
+evaluator = Evaluator(name='MAE')
+# score = evaluator(test['Y'], predictions)
+```
+
+### Workflow 2: Benchmark Evaluation
+
+See `scripts/benchmark_evaluation.py` for a complete example with multiple seeds and proper evaluation protocol.
+
+### Workflow 3: Molecular Generation with Oracles
+
+See `scripts/molecular_generation.py` for an example of goal-directed generation using oracle functions.
+
+## Resources
+
+This skill includes bundled resources for common TDC workflows:
+
+### scripts/
+
+- `load_and_split_data.py`: Template for loading and splitting TDC datasets with various strategies
+- `benchmark_evaluation.py`: Template for running benchmark group evaluations with proper 5-seed protocol
+- `molecular_generation.py`: Template for molecular generation using oracle functions
+
+### references/
+
+- `datasets.md`: Comprehensive catalog of all available datasets organized by task type
+- `oracles.md`: Complete documentation of all 17+ molecule generation oracles
+- `utilities.md`: Detailed guide to data processing, splitting, and evaluation utilities
+
+## Additional Resources
+
+- **Official Website**: https://tdcommons.ai
+- **Documentation**: https://tdc.readthedocs.io
+- **GitHub**: https://github.com/mims-harvard/TDC
+- **Paper**: NeurIPS 2021 - "Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development"