# Deepchem

> Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

- **Type:** Skill
- **Install:** `agentstack add skill-tyche-mkr-scientific-agent-skills-deepchem`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Tyche-MKR](https://agentstack.voostack.com/s/tyche-mkr)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Tyche-MKR](https://github.com/Tyche-MKR)
- **Source:** https://github.com/Tyche-MKR/scientific-agent-skills/tree/main/skills/deepchem

## Install

```sh
agentstack add skill-tyche-mkr-scientific-agent-skills-deepchem
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

**Version note:** Examples target **deepchem 2.8.0** (PyPI stable, Apr 2024). Requires **Python 3.7–3.11** (`5K samples
4. Try GNNs if you have >10K samples
5. Use transfer learning for small datasets or novel scaffolds

### Pattern 4: Handle Imbalanced Data
```python
# Option 1: Balancing transformer
transformer = dc.trans.BalancingTransformer(dataset=train)
train = transformer.transform(train)

# Option 2: Use balanced metrics
metric = dc.metrics.Metric(dc.metrics.balanced_accuracy_score)
```

### Pattern 5: Avoid Memory Issues
```python
# Use DiskDataset for large datasets
dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)

# Use smaller batch sizes
model = dc.models.GCNModel(batch_size=32)  # Instead of 128
```

## Common Pitfalls

### Issue 1: Data Leakage in Drug Discovery
**Problem**: Using random splitting allows similar molecules in train/test sets.
**Solution**: Always use `ScaffoldSplitter` for molecular datasets.

### Issue 2: GNN Underperforming vs Fingerprints
**Problem**: Graph neural networks perform worse than simple fingerprints.
**Solutions**:
- Ensure dataset is large enough (>10K samples typically)
- Increase training epochs (50-100)
- Try different architectures (AttentiveFP, DMPNN instead of GCN)
- Use pretrained models (GROVER)

### Issue 3: Overfitting on Small Datasets
**Problem**: Model memorizes training data.
**Solutions**:
- Use stronger regularization (increase dropout to 0.5)
- Use simpler models (Random Forest instead of deep learning)
- Apply transfer learning (ChemBERTa, GROVER)
- Collect more data

### Issue 4: Import Errors
**Problem**: `No module named 'torch'` / `No module named 'tensorflow'` warnings, or model classes fail to import.
**Solution**: DeepChem loads lazily — install the backend that matches your model, then add the matching extra:
```bash
uv pip install deepchem              # loaders, featurizers, MoleculeNet only
uv pip install 'deepchem[torch]'       # GCN, GAT, AttentiveFP, HuggingFaceModel, GroverModel
uv pip install 'deepchem[tensorflow]'  # legacy Keras models
uv pip install 'deepchem[jax]'         # Haiku/JAX models
```
Install PyTorch or TensorFlow with the correct CUDA build **before** the extra when using GPUs. Quote extras in zsh: `'deepchem[torch]'`.

**Conda + PyTorch users:** If `import deepchem` fails with `undefined symbol: iJIT_NotifyEvent`, pin MKL below 2025 (`conda install "mkl<2025"`) — PyTorch wheels may be incompatible with MKL 2025.0.0.

## Reference Documentation

This skill includes comprehensive reference documentation:

### `references/api_reference.md`
Complete API documentation including:
- All data loaders and their use cases
- Dataset classes and when to use each
- Complete featurizer catalog with selection guide
- Model catalog organized by category (50+ models)
- MoleculeNet dataset descriptions
- Metrics and evaluation functions
- Common code patterns

**When to reference**: Search this file when you need specific API details, parameter names, or want to explore available options.

### `references/workflows.md`
Eight detailed end-to-end workflows:
1. Molecular property prediction from SMILES
2. Using MoleculeNet benchmarks
3. Hyperparameter optimization
4. Transfer learning with pretrained models
5. Molecular generation with GANs
6. Materials property prediction
7. Protein sequence analysis
8. Custom model integration

**When to reference**: Use these workflows as templates for implementing complete solutions.

## Installation

Core package (data loaders, featurizers, MoleculeNet, scikit-learn wrappers):

```bash
uv pip install deepchem
```

Add the extra that matches your model backend (install PyTorch/TensorFlow/JAX first for GPU builds):

```bash
uv pip install 'deepchem[torch]'       # GNNs, TorchModel, HuggingFaceModel, GroverModel
uv pip install 'deepchem[tensorflow]'  # Keras/TensorFlow models
uv pip install 'deepchem[jax]'         # JAX/Haiku models
uv pip install 'deepchem[dqc]'         # Differentiable quantum chemistry (torch + xitorch)
```

Nightly builds: `uv pip install --pre deepchem` (same extras apply with `--pre`).

See [installation guide](https://deepchem.readthedocs.io/en/latest/get_started/installation.html) and [soft requirements](https://deepchem.readthedocs.io/en/latest/requirements.html) for optional dependencies per model class.

## Additional Resources

- Official documentation: https://deepchem.readthedocs.io/
- GitHub repository: https://github.com/deepchem/deepchem
- Tutorials: https://deepchem.readthedocs.io/en/latest/get_started/tutorials.html
- Paper: "MoleculeNet: A Benchmark for Molecular Machine Learning"

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Tyche-MKR](https://github.com/Tyche-MKR)
- **Source:** [Tyche-MKR/scientific-agent-skills](https://github.com/Tyche-MKR/scientific-agent-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-tyche-mkr-scientific-agent-skills-deepchem
- Seller: https://agentstack.voostack.com/s/tyche-mkr
- Browse the marketplace: https://agentstack.voostack.com/browse

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