skills.rest
robinbarvaag
robinbarvaagCOMMUNITY
Explore all skills by this author →
GitHub

pytdc

Accesses PyTDC AI-ready drug discovery datasets and standardized benchmarks for ADME, toxicity, DTI, and molecular generation tasks.

Updated Jan 10, 2026
Search Tags:#machine learning#drug discovery#cheminformatics#benchmarks#datasets#pharmacology
One-click install
npx skills add https://github.com/robinbarvaag/poynt --skill pytdc-robinbarvaag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pytdc
Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/pytdc
Command: npx skills add https://github.com/robinbarvaag/poynt --skill pytdc-robinbarvaag

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides access to curated datasets and benchmarks for drug discovery and development, enabling AI-driven research and prediction.

Core Features & Use Cases

  • Access Diverse Datasets: Utilize datasets for ADME, toxicity, drug-target interactions, molecular generation, and more.
  • Standardized Benchmarks: Evaluate models on predefined tasks with standardized metrics and splits.
  • Use Case: Predict the absorption, distribution, metabolism, and excretion (ADME) properties of novel drug candidates using pre-loaded datasets and established evaluation protocols.

Quick Start

Load the Caco2_Wang ADME dataset and get a scaffold split for training.

Frequently Asked Questions about pytdc

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

FAQPage Schema
Where can I find AI-ready drug discovery datasets for ADME and toxicity prediction?▼

AI-ready drug discovery datasets for ADME and toxicity prediction are available through this Skill, which provides curated benchmarks covering ADME, toxicity, drug-target interactions, and molecular generation tasks.

How do I benchmark machine learning models for pharmacological prediction?▼

To benchmark machine learning models for pharmacological prediction, you can use this Skill to access predefined tasks with standardized metrics and data splits, ensuring consistent evaluation across therapeutic machine learning applications.

Do I need to install the PyTDC library to access cheminformatics datasets?▼

Yes, you need to install the PyTDC library to programmatically access and manipulate the cheminformatics datasets and standardized benchmarks provided by this Skill.

What drug discovery tasks are supported by these machine learning benchmarks?▼

These machine learning benchmarks support drug discovery and development tasks including ADME property prediction, toxicity evaluation, drug-target interactions, and molecular generation.

How do I get scaffold splits for training models on ADME datasets?▼

You can load ADME datasets like Caco2_Wang and retrieve scaffold splits for training by utilizing the programmatic data access functions provided through the PyTDC library.

Can I use this for standardized evaluation of molecular generation tasks?▼

Yes, this Skill facilitates standardized evaluation of molecular generation tasks by providing access to AI-ready datasets with predefined metrics and established evaluation protocols for therapeutic machine learning.

Related Skills

Explore More Education & Research Skills→
jackspacejackspace
16

pytdc

Provide AI-ready drug-discovery datasets and benchmarks from PyTDC for AI/ML modeling.

Community
Intermediate
jimmc414jimmc414
557

pytdc

Load TDC drug discovery datasets with standardized splits and evaluators.

Community
Intermediate
jackspacejackspace
16

molfeat

Convert SMILES strings into machine-learning feature vectors using 100+ featurizers.

Community
Advanced
JosephWoodallJosephWoodall
1

pytdc

Load PyTDC datasets and generate train, valid, and test splits.

Community
Intermediate