Torchdrug
Torchdrug predicts drug-target interactions using deep learning, accelerating drug discovery by identifying potential candidates quickly.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add torchdrug npx -- -y @trustedskills/torchdrug
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"torchdrug": {
"command": "npx",
"args": [
"-y",
"@trustedskills/torchdrug"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill provides access to TorchDrug, a PyTorch library designed for accelerated drug discovery research. It enables users to build and evaluate graph neural networks (GNNs) for tasks like molecular property prediction and drug-target interaction prediction. The tool facilitates the creation of datasets and models tailored to pharmaceutical applications.
When to use it
- Predicting the properties of novel drug candidates based on their chemical structure.
- Identifying potential drug targets by analyzing protein-protein interaction networks.
- Building machine learning models for virtual screening of compound libraries.
- Analyzing large datasets of molecular structures and biological activity data.
Key capabilities
- Graph Neural Network (GNN) model building
- Molecular property prediction
- Drug-target interaction prediction
- Dataset creation and management
Example prompts
- "Build a GNN to predict the solubility of a set of molecules."
- "Create a dataset for drug-target interaction prediction using the provided data files."
- "Train a model to identify potential inhibitors for a specific protein target."
Tips & gotchas
- Requires familiarity with PyTorch and graph neural networks.
- Large datasets may require significant computational resources.
Tags
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