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Zehong-Wang
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pytdc

Load drug discovery datasets and benchmarks from PyTDC.

Updated Aug 27, 2026
Search Tags:#machine learning#drug discovery#cheminformatics#benchmarks#datasets#therapeutic ml
One-click install
npx skills add https://github.com/Zehong-Wang/Kosmos --skill pytdc-zehong-wang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pytdc
Source: https://github.com/Zehong-Wang/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/pytdc
Command: npx skills add https://github.com/Zehong-Wang/Kosmos --skill pytdc-zehong-wang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyTDC, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive platform for accessing and utilizing curated datasets and benchmarks specifically designed for drug discovery and therapeutic machine learning tasks, streamlining the process of model development and validation.

Core Features & Use Cases

  • Access Diverse Datasets: Load datasets for ADME, toxicity, drug-target interactions (DTI), molecular generation, and more.
  • Standardized Benchmarks: Utilize pre-defined benchmark groups for systematic model evaluation.
  • Flexible Data Splitting: Apply various splitting strategies like scaffold, random, and cold splits to ensure robust model generalization.
  • Use Case: A researcher needs to build a model to predict drug toxicity. They can use this Skill to load the ClinTox dataset, apply a scaffold split, train their model, and evaluate it using TDC's built-in metrics.

Quick Start

Load the Caco2_Wang ADME dataset and get a scaffold split.

Frequently Asked Questions about pytdc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I load drug discovery datasets for machine learning benchmarks?▼

This Skill provides access to curated drug discovery datasets for machine learning benchmarks, covering ADME, toxicity, and drug-target interactions to streamline model training and validation.

What is the best way to apply scaffold splits for molecular machine learning models?▼

To apply scaffold splits for molecular machine learning models, this Skill provides standardized data splitting strategies including scaffold, random, and cold splits to ensure robust model generalization during evaluation.

Can I use this Skill to evaluate drug toxicity prediction models?▼

Yes, you can evaluate drug toxicity prediction models by loading datasets like ClinTox, applying a scaffold split, training your model, and measuring performance using built-in evaluation metrics provided by the PyTDC library.

Do I need the PyTDC library to run therapeutic machine learning benchmarks?▼

Yes, the PyTDC library is required as a dependency for data loading, splitting, and analysis when running therapeutic machine learning benchmarks and accessing the standardized drug discovery datasets.

What types of therapeutic machine learning tasks are supported by these benchmarks?▼

These therapeutic machine learning benchmarks support diverse tasks including ADME prediction, toxicity analysis, drug-target interactions (DTI), and molecular generation, providing standardized data splits and evaluation metrics for each.

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