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pharmtox-deeppurpose-dti

Create and validate a DeepPurpose DTI benchmark workflow with strict input validation.

934|152|Updated Mar 1, 2026
Search Tags:#validation#benchmark#ml-pipeline#pharmacology#dti#drug-target-interaction#deeppurpose
One-click install
npx skills add https://github.com/wuyoscar/ISC-Bench --skill pharmtox-deeppurpose-dti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pharmtox-deeppurpose-dti
Source: https://github.com/wuyoscar/ISC-Bench/tree/main/templates/pharmtox_deeppurpose
Command: npx skills add https://github.com/wuyoscar/ISC-Bench --skill pharmtox-deeppurpose-dti

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This ISC template enables researchers to quickly scaffold a pharmacology-focused drug-target interaction (DTI) workflow using DeepPurpose, providing a validated structure for data handling, model training, and result reporting.

Core Features & Use Cases

  • Deploys a complete DTI benchmarking pipeline with a predefined dataset schema and validation rules, ready for customization to new drug-target pairs.
  • Includes example scripts and prompts to demonstrate end-to-end model training, evaluation, and interpretation in pharmacology/toxicology research settings.
  • Use Case: Rapidly prototype a DTI model on a curated positive-controls dataset to benchmark performance and guardrail outputs.

Quick Start

Run the train_dti.py script in this skill directory to initialize and train a DeepPurpose-based DTI model on the provided dataset.

Frequently Asked Questions about pharmtox-deeppurpose-dti

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

FAQPage Schema
How do I benchmark a drug-target interaction model using DeepPurpose?▼

The workflow applies Morgan and Conjoint_triad encodings to represent drug-target pairs, enabling safe, auditable experimentation and model validation for pharmacology and toxicology research.

What input data format is needed for a DeepPurpose DTI pipeline?▼

The included schema ensures data integrity for benchmarking and rapid prototyping across multiple drug-target pairs without manual formatting errors.

Can I use this DTI workflow for toxicology benchmarking without external dependencies?▼

It is designed for rapid prototyping on curated positive-controls datasets to benchmark performance directly.

How does input validation work in a pharmacology machine learning pipeline?▼

This ensures reproducible, auditable experimentation and guards against unexpected errors during DTI benchmarking.

What is the best way to prototype a DTI model for pharmacology research?▼

This approach allows you to benchmark performance and guardrail outputs on curated positive-controls datasets.

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