Skill · em Dados, IA e pesquisa

torchdrug

Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.

Procedência

Antes de instalar

9 arquivos · 94,3 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/torchdrug/.

d=".claude/skills/torchdrug"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/torchdrug"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/core_concepts.md" "$u/references/core_concepts.md" \
  -o "$d/references/datasets.md" "$u/references/datasets.md" \
  -o "$d/references/knowledge_graphs.md" "$u/references/knowledge_graphs.md" \
  -o "$d/references/models_architectures.md" "$u/references/models_architectures.md" \
  -o "$d/references/molecular_generation.md" "$u/references/molecular_generation.md" \
  -o "$d/references/molecular_property_prediction.md" "$u/references/molecular_property_prediction.md" \
  -o "$d/references/protein_modeling.md" "$u/references/protein_modeling.md" \
  -o "$d/references/retrosynthesis.md" "$u/references/retrosynthesis.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/torchdrug/.

d="$HOME/.claude/skills/torchdrug"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/torchdrug"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/core_concepts.md" "$u/references/core_concepts.md" \
  -o "$d/references/datasets.md" "$u/references/datasets.md" \
  -o "$d/references/knowledge_graphs.md" "$u/references/knowledge_graphs.md" \
  -o "$d/references/models_architectures.md" "$u/references/models_architectures.md" \
  -o "$d/references/molecular_generation.md" "$u/references/molecular_generation.md" \
  -o "$d/references/molecular_property_prediction.md" "$u/references/molecular_property_prediction.md" \
  -o "$d/references/protein_modeling.md" "$u/references/protein_modeling.md" \
  -o "$d/references/retrosynthesis.md" "$u/references/retrosynthesis.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/torchdrug/.

d=".agents/skills/torchdrug"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/torchdrug"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/core_concepts.md" "$u/references/core_concepts.md" \
  -o "$d/references/datasets.md" "$u/references/datasets.md" \
  -o "$d/references/knowledge_graphs.md" "$u/references/knowledge_graphs.md" \
  -o "$d/references/models_architectures.md" "$u/references/models_architectures.md" \
  -o "$d/references/molecular_generation.md" "$u/references/molecular_generation.md" \
  -o "$d/references/molecular_property_prediction.md" "$u/references/molecular_property_prediction.md" \
  -o "$d/references/protein_modeling.md" "$u/references/protein_modeling.md" \
  -o "$d/references/retrosynthesis.md" "$u/references/retrosynthesis.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/torchdrug/.

d="$HOME/.agents/skills/torchdrug"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/torchdrug"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/core_concepts.md" "$u/references/core_concepts.md" \
  -o "$d/references/datasets.md" "$u/references/datasets.md" \
  -o "$d/references/knowledge_graphs.md" "$u/references/knowledge_graphs.md" \
  -o "$d/references/models_architectures.md" "$u/references/models_architectures.md" \
  -o "$d/references/molecular_generation.md" "$u/references/molecular_generation.md" \
  -o "$d/references/molecular_property_prediction.md" "$u/references/molecular_property_prediction.md" \
  -o "$d/references/protein_modeling.md" "$u/references/protein_modeling.md" \
  -o "$d/references/retrosynthesis.md" "$u/references/retrosynthesis.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/torchdrug/.

d=".agents/skills/torchdrug"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/torchdrug"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/core_concepts.md" "$u/references/core_concepts.md" \
  -o "$d/references/datasets.md" "$u/references/datasets.md" \
  -o "$d/references/knowledge_graphs.md" "$u/references/knowledge_graphs.md" \
  -o "$d/references/models_architectures.md" "$u/references/models_architectures.md" \
  -o "$d/references/molecular_generation.md" "$u/references/molecular_generation.md" \
  -o "$d/references/molecular_property_prediction.md" "$u/references/molecular_property_prediction.md" \
  -o "$d/references/protein_modeling.md" "$u/references/protein_modeling.md" \
  -o "$d/references/retrosynthesis.md" "$u/references/retrosynthesis.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/torchdrug/.

d="$HOME/.gemini/antigravity-cli/skills/torchdrug"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/torchdrug"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/core_concepts.md" "$u/references/core_concepts.md" \
  -o "$d/references/datasets.md" "$u/references/datasets.md" \
  -o "$d/references/knowledge_graphs.md" "$u/references/knowledge_graphs.md" \
  -o "$d/references/models_architectures.md" "$u/references/models_architectures.md" \
  -o "$d/references/molecular_generation.md" "$u/references/molecular_generation.md" \
  -o "$d/references/molecular_property_prediction.md" "$u/references/molecular_property_prediction.md" \
  -o "$d/references/protein_modeling.md" "$u/references/protein_modeling.md" \
  -o "$d/references/retrosynthesis.md" "$u/references/retrosynthesis.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.torchdrug

Prévia do SKILL.md

---
name: torchdrug
description: "Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs."
---

# TorchDrug

## Overview

TorchDrug is a comprehensive PyTorch-based machine learning toolbox for drug discovery and molecular science. Apply graph neural networks, pre-trained models, and task definitions to molecules, proteins, and biological knowledge graphs, including molecular property prediction, protein modeling, knowledge graph reasonin…

## When to Use This Skill

This skill should be used when working with:

**Data Types:**
- SMILES strings or molecular structures
- Protein sequences or 3D structures (PDB files)
- Chemical reactions and retrosynthesis
- Biomedical knowledge graphs
- Drug discovery datasets

**Tasks:**
- Predicting molecular properties (solubility, toxicity, activity)
- Protein function or structure prediction
- Drug-target binding prediction
- Generating new molecular structures
- Planning chemical synthesis routes
- Link prediction in biomedical knowledge bases
- Training graph neural networks on scientific data

**Libraries and Integration:**
- TorchDrug is the primary library
- Often used with RDKit for cheminformatics
- Compatible with PyTorch and PyTorch Lightning
- Integrates with AlphaFold and ESM for proteins

## Getting Started

### Installation
…

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