Agent skill · research science · k-dense-ai
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.
Why this skill is useful
Adds three executable scripts for molecular property prediction and graph neural network training that aren't in public docs.
What it needs
Requires jax, python, pytorch, rdkit, tensorflow installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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 (<3.12 on PyPI). Core utilities (loaders, featurizers, MoleculeNet) work without a DL backend; GNN and transformer models need the matching extra (torch, tensorflow, or jax). Install the backend framework first when using GPU builds. 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 Eight capability areas, each with worked code, are in references/corecapabilities.md: 1. Molecular data loading and processing — loaders, NumpyDataset / DiskDataset. 2. Molecular featurization — circular fingerprints, graph convolution, and descriptors. 3. Data splitting — random, scaffold, stratified, and butina splitters, and why scaffold splitting is the honest default for molecules. 4. Model selection and training — the model families and how to fit them. 5. MoleculeNet benchmarks — loading standard datasets and their published splits. 6. Transfer learning — pretraining and fine-tuning. 7. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/deepchem