compchem-pytdc-dataset

Validate PyTDC toxicity templates and generate reproducible data quality reports.

934|152|Updated Mar 1, 2026

Search Tags:#benchmark #validate #chemistry #datasets #pytdc #data-validation #toxicity

Ready to run

npx skills add https://github.com/wuyoscar/ISC-Bench --skill compchem-pytdc-dataset

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Please help me install this Agent Skill.
Skill: compchem-pytdc-dataset
Source: https://github.com/wuyoscar/ISC-Bench/tree/main/templates/compchem_pytdc
Command: npx skills add https://github.com/wuyoscar/ISC-Bench --skill compchem-pytdc-dataset

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps researchers validate and benchmark PyTDC toxicity templates used in computational chemistry tasks, ensuring data quality and reproducibility across experiments.

Core Features & Use Cases

Validate toxicity datasets against PyTDC constraints to ensure consistency.
Generate validation reports and quick summaries for multiple compounds.
Use Case: Prepare a reproducible toxicity benchmarking workflow for a new dataset.

Quick Start

Run the validation workflow on the provided PyTDC toxicity templates to generate a reproducible data quality report.

Frequently Asked Questions about compchem-pytdc-dataset

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

FAQPage Schema

How do I validate PyTDC toxicity datasets for computational chemistry workflows?▼

To validate PyTDC toxicity datasets, you can run domain-specific validation checks against PyTDC constraints to ensure data consistency, generating structured validation reports and executable summaries for downstream benchmarking.

What is the best way to ensure reproducibility when benchmarking toxicity datasets in chemistry?▼

The best way to ensure reproducibility when benchmarking toxicity datasets is to enforce domain-specific validators that check data against PyTDC constraints, producing structured results suitable for consistent downstream analysis.

Can I use custom compounds when validating PyTDC toxicity templates?▼

Yes, you can validate PyTDC toxicity templates using custom compounds. The validation workflow supports a range of custom compound checks to ensure dataset quality and consistency across experiments.

How does PyTDC data validation integrate with existing computational chemistry tooling?▼

PyTDC data validation integrates with existing PyTDC tooling by enforcing domain-specific validators and outputting structured results, ensuring validated toxicity datasets flow seamlessly into downstream computational chemistry workflows.

What do I need to prepare before running a PyTDC toxicity data validation workflow?▼

Before running a PyTDC toxicity data validation workflow, you need PyTDC toxicity templates and any custom compounds prepared for testing, allowing the skill to generate a reproducible data quality report.

Why does my toxicity benchmarking workflow produce inconsistent results across experiments?▼

Inconsistent toxicity benchmarking results often occur when datasets lack proper validation. Running PyTDC constraint checks and domain-specific validators ensures data consistency and reproducibility across experiments.