Skill · em Dados, IA e pesquisa

deepchem

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Procedência

Origem: davila7/claude-code-templates
Caminho: cli-tool/components/skills/scientific/deepchem
Versão fixada: 57f899e5394bb8ca166f38eacae8f0853cbfe033
Licença: MIT
Espelhado em 25/09/2026
nenhum download no Claude Code Templates (lido em 25/09/2026)

Antes de instalar

6 arquivos · 66,9 KB · inclui 3 scripts que executam: scripts/graph_neural_network.py, scripts/predict_solubility.py, scripts/transfer_learning.py

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/deepchem/.

d=".claude/skills/deepchem"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/deepchem"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/graph_neural_network.py" "$u/scripts/graph_neural_network.py" \
  -o "$d/scripts/predict_solubility.py" "$u/scripts/predict_solubility.py" \
  -o "$d/scripts/transfer_learning.py" "$u/scripts/transfer_learning.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

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

d="$HOME/.claude/skills/deepchem"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/deepchem"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/graph_neural_network.py" "$u/scripts/graph_neural_network.py" \
  -o "$d/scripts/predict_solubility.py" "$u/scripts/predict_solubility.py" \
  -o "$d/scripts/transfer_learning.py" "$u/scripts/transfer_learning.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

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

d=".agents/skills/deepchem"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/deepchem"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/graph_neural_network.py" "$u/scripts/graph_neural_network.py" \
  -o "$d/scripts/predict_solubility.py" "$u/scripts/predict_solubility.py" \
  -o "$d/scripts/transfer_learning.py" "$u/scripts/transfer_learning.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

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

d="$HOME/.agents/skills/deepchem"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/deepchem"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/graph_neural_network.py" "$u/scripts/graph_neural_network.py" \
  -o "$d/scripts/predict_solubility.py" "$u/scripts/predict_solubility.py" \
  -o "$d/scripts/transfer_learning.py" "$u/scripts/transfer_learning.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

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

d=".agents/skills/deepchem"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/deepchem"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/graph_neural_network.py" "$u/scripts/graph_neural_network.py" \
  -o "$d/scripts/predict_solubility.py" "$u/scripts/predict_solubility.py" \
  -o "$d/scripts/transfer_learning.py" "$u/scripts/transfer_learning.py" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

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

d="$HOME/.gemini/antigravity-cli/skills/deepchem"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/deepchem"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/api_reference.md" "$u/references/api_reference.md" \
  -o "$d/references/workflows.md" "$u/references/workflows.md" \
  -o "$d/scripts/graph_neural_network.py" "$u/scripts/graph_neural_network.py" \
  -o "$d/scripts/predict_solubility.py" "$u/scripts/predict_solubility.py" \
  -o "$d/scripts/transfer_learning.py" "$u/scripts/transfer_learning.py" \
  -o "$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.deepchem

Prévia do SKILL.md

---
name: deepchem
description: "Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML."
---

# DeepChem

## Overview

DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.

## When to Use This Skill

This skill should be used when:
- Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
- Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
- Training models on chemical/biological datasets
- Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
- Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)
- Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)
- Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)
- Predicting crystal/materials properties (bandgap, formation energy)
- Analyzing protein or DNA sequences

## Core Capabilities

### 1. Molecular Data Loading and Processing

DeepChem provides specialized loaders for various chemical data formats:

```python
import deepchem as dc

# Load CSV with SMILES
featurizer = dc.feat.CircularFingerprint(radius=2, size=2048)
loader = dc.data.CSVLoader(
    tasks=['solubility', 'toxicity'],
    feature_field='smiles',
    featurizer=featurizer
)
…

Ver todo o marketplace