PyTDC (Therapeutics Data Commons)
Skill Verifiziert AktivTherapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
To empower AI agents with readily accessible and standardized drug discovery datasets, facilitating research in therapeutic ML and pharmacological prediction.
Funktionen
- Access to curated drug discovery datasets (ADME, Tox, DTI, etc.)
- Standardized data splitting methods (scaffold, cold-drug, cold-target)
- Integrated model evaluation metrics
- Data processing utilities (molecule conversion, filtering)
Anwendungsfälle
- Working with AI-ready drug discovery datasets
- Benchmarking machine learning models on pharmaceutical tasks
- Predicting molecular properties and interactions
- Generating novel molecules with desired characteristics
Nicht-Ziele
- Performing wet-lab experiments
- Deploying trained models
- Providing extensive molecular visualization beyond basic dataframes
Workflow
- Load a specific dataset using its name
- Split the dataset into train/validation/test sets using a chosen method
- Process or convert data as needed (e.g., to graphs)
- Train a machine learning model on the prepared data
- Evaluate the model using provided metrics
Voraussetzungen
- uv
- Python 3.11+
Scope
- info:Tool surface sizeThe skill primarily exposes functions for data loading and splitting, with a focused tool surface rather than a large number of distinct commands.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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