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torchdrug

Predict molecular properties from SMILES strings using a PyTorch-based toolkit.

557|98|Updated Nov 7, 2025
Search Tags:#drug discovery#cheminformatics#AI scientist#molecular modeling#knowledge graphs#protein analysis
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill torchdrug-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchdrug
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/torchdrug
Command: npx skills add https://github.com/jimmc414/Kosmos --skill torchdrug-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for accelerating drug discovery and molecular science by leveraging advanced machine learning models on chemical and biological data.

Core Features & Use Cases

  • Molecular Property Prediction: Predict properties like solubility, toxicity, and binding affinity.
  • Protein Modeling: Analyze protein sequences and structures for function prediction.
  • Knowledge Graph Reasoning: Discover relationships in biomedical data for drug repurposing.
  • Molecular Generation & Retrosynthesis: Design novel molecules and plan their synthesis.
  • Use Case: Predict the blood-brain barrier penetration for a new drug candidate using its molecular structure.

Quick Start

Use the torchdrug skill to predict molecular properties for a list of SMILES strings.

Frequently Asked Questions about torchdrug

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

FAQPage Schema
How do I predict molecular properties like solubility or toxicity using SMILES strings?▼

Molecular property prediction uses graph neural networks and pre-trained models on PyTorch to estimate attributes like solubility, toxicity, and binding affinity directly from SMILES strings.

Can I use PyTorch for protein sequence analysis and function prediction?▼

Yes, you can use a PyTorch-based toolkit for protein modeling to analyze protein sequences and structures, enabling function prediction and integration with other biomedical graph data.

How does knowledge graph reasoning help with drug repurposing in biomedical data?▼

Knowledge graph reasoning discovers hidden relationships in biomedical data, enabling drug repurposing by mapping interactions across molecular and protein networks using PyTorch-based machine learning models.

What is the best way to design novel molecules and plan their retrosynthesis?▼

Molecular generation and retrosynthesis use PyTorch-based graph neural networks to design novel molecules and computationally plan their synthesis pathways from pre-trained molecular representations.

Does this AI-driven drug discovery toolkit support pre-trained models for blood-brain barrier penetration prediction?▼

Yes, the toolkit supports pre-trained models and diverse datasets for predicting complex properties like blood-brain barrier penetration directly from a drug candidate's molecular structure.

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