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. Part of the AlterLab Academic Skills suite.
To empower researchers in drug discovery and therapeutic ML by providing easy access to standardized datasets, benchmarks, and evaluation tools.
Funktionen
- Access AI-ready drug discovery datasets (ADME, toxicity, DTI)
- Utilize standardized benchmarks and data splits
- Perform molecular property prediction and generation
- Integrates with the PyTDC Python library for programmatic access
Anwendungsfälle
- Working with drug discovery or therapeutic ML datasets
- Benchmarking machine learning models on pharmaceutical tasks
- Predicting molecular properties (ADME, toxicity, bioactivity)
- Accessing curated datasets with proper train/test splits
Nicht-Ziele
- Performing actual wet-lab experiments
- Replacing dedicated cheminformatics software for advanced molecular modeling
- Providing real-time clinical decision support
Workflow
- Install PyTDC via pip
- Import relevant modules from tdc
- Load a specific dataset (e.g., ADME, DTI)
- Get data split (scaffold, random, cold splits)
- Process data (convert formats, filter molecules)
- Train a model (user-implemented)
- Evaluate model using TDC evaluators
Praktiken
- Data Curation
- Machine Learning Benchmarking
- Reproducible Research
Voraussetzungen
- Python environment
- pip or uv for package installation
- PyTDC library installed
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-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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