Load and analyze Therapeutics Data Commons drug discovery datasets and benchmarks.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pytdc-jasrajtulsi
Please help me install this Agent Skill. Skill: pytdc Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/pytdc Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill pytdc-jasrajtulsi
It helps researchers and ML practitioners work with curated therapeutics datasets and benchmarks without spending time manually handling dataset discovery, splitting strategies, evaluation setup, or oracle-based molecular scoring.
Use this skill to load the right TDC dataset, choose the appropriate split and metric, and generate a benchmark-ready evaluation workflow for your therapeutic ML experiment.
High-intent search queries and answers about installing and using this skill.
You can load and benchmark ADME and toxicity datasets for drug discovery by using this Skill to access Therapeutics Data Commons workflows, which provide a consistent interface for dataset retrieval, standardized splits, and molecular oracle evaluation.
The best way to apply scaffold and cold splits to molecular generation datasets is through the built-in splitting strategies provided by the Therapeutics Data Commons workflows, which ensure standardized partitioning for reliable ML model evaluation.
Yes, you can calculate multi-seed benchmark metrics for therapeutic ML models because the Skill supports multi-seed benchmark-group evaluation, allowing you to report reliable mean and standard deviation scores across different random seeds.
Yes, this Skill supports oracle functions for molecular optimization by providing label transformations and utility helpers to evaluate drug-likeness, target activity, and generation experiments within the Therapeutics Data Commons framework.
Yes, you need TDC-compatible Python dependencies including numpy, pandas, and scipy to run Therapeutics Data Commons workflows for numerical analysis, tabular processing, and scientific evaluation of drug discovery datasets.
Benchmarking drug discovery datasets with TDC eliminates manual dataset discovery, splitting strategy design, and evaluation setup by providing curated datasets, standardized metrics, and consistent interfaces directly within your Python environment.
Yeachan-HeoLoad, clean, and summarize tabular datasets with descriptive statistics.
JunMA98Generate publication-ready CS visualizations with accessible color palettes and export formats.
dralkhLoad therapeutics data commons datasets and run evaluation workflows for drug discovery.
K-Dense-AILoad AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons.
