Deepchem

Claude Plugin for CompChem , Drug Discovery & Organic Chemistry reasoning

Install

npx -y skills add Kdevos12/ALKYL --skill deepchem

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Use when working with DeepChem for molecular machine learning, drug discovery, quantum chemistry, materials science, or bioinformatics. Handles molecular datasets, featurization strategies, model training/evaluation, and predictions on chemical data.

SKILL.md

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DeepChem

Deep learning for the life sciences: drug discovery, quantum chemistry, materials science, bioinformatics.

When to Use This Skill

Building ML models on molecular datasets (SMILES, graphs, fingerprints)
Working with MoleculeNet benchmark datasets
Predicting molecular properties (solubility, toxicity, binding affinity)
Protein-ligand interaction modeling
Quantum chemistry property prediction (QM9, GDB datasets)
Featurizing molecules for downstream ML tasks
Virtual screening and drug discovery pipelines

Quick Start — Standard Workflow

import deepchem as dc

# 1. Load dataset with featurizer
tasks, datasets, transformers = dc.molnet.load_delaney(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = datasets

# 2. Create model
model = dc.models.GraphConvModel(n_tasks=1, mode='regression', dropout=0.2)

# 3. Train
model.fit(train_dataset, nb_epoch=100)

# 4. Evaluate
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)
train_score = model.evaluate(train_dataset, [metric], transformers)
test_score  = model.evaluate(test_dataset,  [metric], transformers)

# 5. Predict
predictions = model.predict_on_batch(test_dataset.X[:10])

Router — What to Read

Task	Reference
Dataset creation, access, splitters	`references/core-concepts.md`
Training workflow, metrics, hyperopt, multitask	`references/model-training.md`
Fingerprints, GCN, ChemBERTa, graph models	`references/mol-machine-learning.md`
MoleculeNet, protein-ligand, virtual screening	`references/drug-discovery.md`
QM9, DeepQMC, materials science	`references/quantum-materials.md`

Installation

pip install --pre deepchem          # with TensorFlow
pip install --pre deepchem[torch]   # with PyTorch
pip install --pre deepchem[jax]     # with JAX

import deepchem as dc
dc.__version__   # verify installation

Key Submodules

Submodule	Role
`dc.molnet`	MoleculeNet dataset loaders
`dc.models`	All model classes
`dc.feat`	Featurizers
`dc.metrics`	Evaluation metrics
`dc.splits`	Dataset splitters
`dc.data`	Dataset classes
`dc.trans`	Transformers (normalization, etc.)

Related Skills

rdkit-patterns - Molecular manipulation before DeepChem ingestion
cheminformatics - SMILES, molecular representations reference