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.
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill deepchemnpx skills add K-Dense-AI/scientific-agent-skills --skill deepchem -a claude-code -gnpx skills add K-Dense-AI/scientific-agent-skills --skill deepchem -a codex -ggemini skills install https://github.com/K-Dense-AI/scientific-agent-skills.git --path scientific-skills/deepchemgh skill install K-Dense-AI/scientific-agent-skills deepchemgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/K-Dense-AI__scientific-agent-skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/K-Dense-AI__scientific-agent-skills/scientific-skills/deepchem ~/.claude/skills/deepchem # Claude Code
cp -R /tmp/K-Dense-AI__scientific-agent-skills/scientific-skills/deepchem ~/.agents/skills/deepchem # Codex, Cursor, Gemini CLI, Copilot, OpenCode