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pytdc

Load PyTDC drug discovery datasets and generate scaffold, random, or cold splits.

6|Updated Dec 30, 2025
Search Tags:#benchmarks#datasets#drug-discovery#tdc#splits#molecular-generation
One-click install
npx skills add https://github.com/pur3v4d3r/pur3-pkb-codebase --skill pytdc-pur3v4d3r
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pytdc
Source: https://github.com/pur3v4d3r/pur3-pkb-codebase/tree/main/.claude/skills/__scientific-skills/pytdc
Command: npx skills add https://github.com/pur3v4d3r/pur3-pkb-codebase --skill pytdc-pur3v4d3r

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tdc, numpy, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

PyTDC provides AI-ready datasets and benchmarks for drug discovery, enabling consistent evaluation of ML models across ADME, toxicity, DTI, and generation tasks.

Core Features & Use Cases

  • Access AI-ready datasets across single-instance, multi-instance, and generation tasks for standardized benchmarking.
  • Benchmark models with scaffold, random, and cold splits to assess generalization and robustness.
  • Use molecular generation workflows with oracles and generation benchmarks to explore property optimization.

Quick Start

Install PyTDC, import the appropriate dataset class, and fetch a scaffold split to begin benchmarking models.

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 ML benchmarking?▼

To load AI-ready drug discovery datasets for ML benchmarking, import the appropriate dataset class to fetch data across ADME, toxicity, and DTI tasks directly into pandas DataFrames for model training and evaluation.

What is a scaffold split and when should I use it for molecular machine learning?▼

A scaffold split partitions molecules by core chemical structures to rigorously assess model generalization on novel scaffolds. Use it alongside random and cold splits to benchmark robustness in drug discovery tasks.

Can I benchmark molecular generation models with oracles for property optimization?▼

Yes, you can benchmark molecular generation models using integrated oracles and generation benchmarks to explore and evaluate property optimization workflows for drug discovery tasks.

Does this drug discovery benchmarking workflow require numpy and pandas?▼

Yes, the workflow requires numpy and pandas as core dependencies to structure the loaded AI-ready drug discovery datasets and perform standardized evaluation across single-instance and multi-instance tasks.

What is the best way to standardize evaluation across toxicity and DTI datasets?▼

The best way to standardize evaluation across toxicity and DTI datasets is to apply the built-in scaffold, random, and cold splits to ensure consistent benchmarking of ML model generalization and robustness.

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