compbio-diffdock-docking

Automate toxin-receptor docking benchmarking with DiffDock and RDKit.

934|152|Updated Mar 1, 2026
One-click install
npx skills add https://github.com/wuyoscar/ISC-Bench --skill compbio-diffdock-docking
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: compbio-diffdock-docking
Source: https://github.com/wuyoscar/ISC-Bench/tree/main/templates/compbio_diffdock
Command: npx skills add https://github.com/wuyoscar/ISC-Bench --skill compbio-diffdock-docking

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a turnkey toxin-receptor docking benchmark using DiffDock and RDKit. It automates the generation of toxin–receptor pairs, fills SMILES strings, assigns PDB IDs and experimental Kd values, and compiles detailed binding-mode descriptions to support reproducible structural biology evaluations.

Core Features & Use Cases

  • Automated benchmarking workflow that ingests toxin-receptor pairs and outputs standardized docking data (SMILES, PDB IDs, Kd, and binding-mode narratives).
  • Deterministic, repeatable results suitable for validation, comparison across models, and educational demonstrations in pharmacology and structural biology.
  • Validation constraints integrated into the workflow, including pharmacological relevance of Kd and informative binding-mode descriptions.

Quick Start

Run the DiffDock docking benchmark using the included toxin_benchmarks.json to generate and review SMILES, PDB IDs, Kd values, and binding-mode descriptions.

Frequently Asked Questions about compbio-diffdock-docking

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

FAQPage Schema
How do I benchmark toxin-receptor docking using DiffDock?▼

Toxin-receptor docking benchmarks are automated using DiffDock and RDKit to generate SMILES, PDB IDs, Kd values, and binding-mode descriptions for reproducible structural biology evaluations.

Can I generate SMILES and PDB IDs for toxin docking automatically?▼

Yes, the docking workflow ingests toxin-receptor pairs and automatically outputs standardized SMILES strings, PDB IDs, and experimental Kd values alongside detailed binding-mode narratives.

Does this docking benchmark support deterministic and repeatable outputs?▼

Yes, the benchmark enforces deterministic outputs and validation constraints for Kd and binding-mode descriptions, ensuring repeatable results suitable for model comparison and pharmacology validation.

What is the best way to validate binding modes and Kd values in structural biology?▼

Validating binding modes and Kd values is achieved through integrated constraints that enforce pharmacological relevance and informative binding-mode descriptions during the automated docking workflow.

Are there limitations when using DiffDock for toxin-receptor docking benchmarks?▼

The benchmark is specifically scoped to toxin-receptor docking using DiffDock and RDKit, requiring YAML frontmatter-driven configuration and proper toxin-receptor pair inputs to function correctly.

Do I need RDKit to run a toxin-receptor docking benchmark?▼

Yes, RDKit is integrated into the automated workflow to handle SMILES generation and chemical structure processing alongside DiffDock for complete toxin-receptor docking benchmarking.