# Deepchem

Builds molecular property prediction and MoleculeNet workflows with DeepChem, including...

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Data and ML
- License: MIT license
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill deepchem -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill deepchem -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill deepchem -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill deepchem -g -a gemini-cli -y`

## Description

Builds molecular property prediction and MoleculeNet workflows with DeepChem, including SMILES featurization, scaffold or grouped holdouts, masked labels, graph models and explicit pretrained encoder transfer. Used for ADMET, toxicity, solubility and chemistry ML when DeepChem data/model contracts and scientific validation are needed.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# DeepChem

## When to use

Use for molecular property prediction, SMILES/graph featurization, MoleculeNet
benchmarks and explicitly configured encoder transfer. The workflow also covers
DeepChem materials and sequence adapters when their data/model contracts are met.

Targets **DeepChem 2.8.0**, still the stable PyPI release at review. Nightly 2.8.1
builds have additional APIs and different constraints; do not mix `latest` docs
with a stable installation. The tested CPU stack and optional-backend limitations
are in [references/review.md](references/review.md).

## Workflow

1. Define compound identity, assay conditions, target units and missing labels.
   Parse SMILES and retain a record of rejected rows. Audit duplicate structures
   and repeated measurements before splitting.
2. Match the representation to the model using the table below. Fit a numeric
   baseline first; dataset size alone does not select the best architecture.
3. Choose a scaffold, temporal or grouped holdout for the deployment question.
   Preserve exact split IDs, inspect scaffold overlap and per-task class support,
   and reject empty splits. Scaffold splitting does not eliminate all leakage.
4. Fit preprocessing on training data only. Preserve `dataset.w` masks. Normalize
   continuous targets only, and invert those transforms for metrics/predictions.
5. Select model settings/epochs on validation data and evaluate the final holdout
   once. Report per-task support, uncertainty across planned repeats and baseline
   comparisons. AUC requires both observed classes.
6. Predict using the training representation and transforms. Preserve identifiers,
   original target units and applicability limits. Successful fitting is not
   evidence of prospective scientific performance.

## Essential model contracts

The rest: https://github.com/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/deepchem/SKILL.md