deepchem

combined

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML. 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.

When to use

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)

Skill page

Conflict

The two skills cover the same DeepChem domain but with materially different procedures. The K-Dense copy is a strict, version-pinned scientific validation workflow (DeepChem 2.8.0, Python 3.11, explicit model/featurizer contracts, scaffold-split leakage checks, masked-label handling, and a bundled predict_solubility.py script). The OpenClaw copy is a broader, version-agnostic tutorial with a featurizer decision tree, a dataset-size model-selection table, and code examples for loaders, splitters, and multiple model families. Neither is a reproduction of the other; crowning one would hide the other's real alternative steps (e.g., the OpenClaw dataset-size table and the K-Dense explicit model contracts and pinned installation).