How does PyTDC handle data splitting for chemistry?

It supports advanced, domain-relevant splitting strategies such as scaffold-based splits for chemical diversity and 'cold-split' methods for evaluating performance on unseen drugs or targets.

What kind of datasets does the PyTDC skill provide?

It provides curated, AI-ready datasets for single-instance prediction (ADME, toxicity, HTS, QM), multi-instance prediction (DTI, DDI, PPI), and molecular generation tasks.

Can I use this skill for molecular property optimization?

Yes, the skill includes access to over 17 molecular oracle functions that can be used to score and optimize molecules for specific biological and chemical properties.

What are PyTDC Benchmark Groups?

Benchmark Groups are curated collections of datasets (like the ADMET group) with standardized evaluation protocols and metrics designed to enable fair model comparison.

PyTDC (Therapeutics Data Commons)

Name: PyTDC (Therapeutics Data Commons)
Author: x-cmd

byx-cmd

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データサイエンスとML

Provides AI-ready datasets, benchmarks, and molecular oracles for drug discovery and therapeutic machine learning.

概要

PyTDC is an open-science platform designed to streamline drug discovery workflows by providing access to high-quality, curated datasets and standardized evaluation metrics. It covers the entire therapeutics pipeline, including molecular property prediction, drug-target interactions, and molecule generation. By offering specialized data splits like scaffold and cold-start, along with built-in molecular oracles for property optimization, PyTDC enables researchers to benchmark machine learning models accurately and accelerate the development of novel pharmacological treatments.

主な機能

8 GitHub stars
Apply domain-specific data splits including scaffold, temporal, and cold-start strategies
Implement standardized benchmark groups with 5-seed evaluation protocols
Predict drug-target (DTI) and drug-drug (DDI) interactions with multi-instance learning
Access curated datasets for ADME, toxicity, and bioactivity prediction
Utilize molecular oracles for goal-directed molecule generation and optimization

ユースケース

Predicting pharmacokinetic properties like intestinal permeability and blood-brain barrier penetration
Benchmarking machine learning architectures on standardized pharmaceutical datasets
Generating and optimizing novel molecules with specific binding affinities and safety profiles

概要

主な機能

8 GitHub stars
Apply domain-specific data splits including scaffold, temporal, and cold-start strategies
Implement standardized benchmark groups with 5-seed evaluation protocols
Predict drug-target (DTI) and drug-drug (DDI) interactions with multi-instance learning
Access curated datasets for ADME, toxicity, and bioactivity prediction
Utilize molecular oracles for goal-directed molecule generation and optimization

ユースケース

Predicting pharmacokinetic properties like intestinal permeability and blood-brain barrier penetration
Benchmarking machine learning architectures on standardized pharmaceutical datasets
Generating and optimizing novel molecules with specific binding affinities and safety profiles