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Torchdrug

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davila7·vlatest·ai-ml·MIT

Torchdrug predicts drug-target interactions using deep learning, accelerating drug discovery by identifying potential candidates quickly.

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Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill torchdrug -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

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Live repository

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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.