review.md
87 lines · skills/deepchem/references/review.md at 5f2bc03
Review evidence — 2026-09-30
Raw lines 1 to 2Release and executed environment
The current PyPI stable release remains DeepChem 2.8.0 (Python >=3.7,<3.12). The audit used isolated Python 3.11.11, Torch 2.14.1, Transformers 5.18.0, torch-geometric 2.8.0.post1, NumPy 2.4.6, scikit-learn 1.9.1, and RDKit 2026.3.6. These are the executed versions, not universal compatibility claims. The current DeepChem nightly is a different installation target.
DeepChem logs skipped optional TensorFlow/JAX/DGL/Lightning modules. Its bundled Chemberta class also imports an obsolete Transformers tokenizer submodule under Transformers 5; the tested script uses the working generic HuggingFaceModel wrapper with Transformers model/tokenizer objects instead. Missing optional-class warnings did not prevent the tested core paths.
Executed synthetic checks
The repository test suite exercises:
- 2048-bit fingerprints, MoleculeNet alias differences, all five graph feature contracts and GROVER feature dimensions.
- CSV duplicate headers, invalid structures, non-finite labels, missing-label masks, split row conservation, and HF rejection of unsupported sparse/multitask input.
- One-epoch CPU solubility regression plus new-molecule prediction, with target normalization undone. The script reshapes the released regressor's trailing singleton output to one value per molecule.
- Binary DMPNN fit/predict and scoring. Single-task predictions have shape
(n,2); the shared scorer handles that and(n,tasks,2)without confusing classes/tasks. - Tiny local randomly initialized RoBERTa classification and regression checkpoints, locally saved tokenizer/model loading, HF fit and logits-aware scoring.
- A synthetic GROVER encoder state dictionary restored strictly into a matching architecture, followed by CPU classification and regression fits. This checks transfer mechanics, not a public pretrained GROVER model.
- Original-unit regression metrics, one-class AUC handling, numeric RF baseline, keyword-based grid search, and a custom Torch network using DeepChem
L2Loss. - All three command-line help paths and conflicting-input rejection.
A separate tiny MoLFormer smoke used the current official repository's Python source at commit 361063d0ad524ef77cf39b08469f6be770dc550f, reviewed before importing, with a reduced random configuration and synthetic tokenizer. The local checkpoint was loaded and fine-tuned through the script on CPU. No public model weights were downloaded. MoLFormer does not accept token_type_ids; the script selects only input_ids/attention_mask and enables deterministic_eval=True.
Synthetic fits prove executable interfaces only. They do not measure generalization, convergence, chemical applicability, model quality or scientific benchmark scores.
Raw lines 17 to 48Live public read probes
The seven released MoleculeNet source URLs used by the scripts returned HTTP 206 for a bounded first-2048-byte request. CSV or decompressed gzip headers matched ESOL, Tox21, BBBP, BACE, HIV, FreeSolv and Lipophilicity task/SMILES fields. These were unauthenticated partial content probes, not full dataset downloads or benchmark training. No API credentials or paid scientific service calls were used.
The current MoLFormer Hub configuration and Python source files resolved at the same commit; the configuration supplies AutoModelForSequenceClassification. The former ibm/... model URL redirects to ibm-research/.... Its generic fast tokenizer is configured by tokenizer_config.json; a guessed tokenization_molformer.py returned 404 and is not required or referenced by the shipped workflow. The current card documents a compat-v4 branch for Transformers 4.
Unexecuted routes
DGL/DGL-LifeSci were absent on this macOS host: GCN, GAT, AttentiveFP and Torch MPNN constructors/training were checked against released source, while their featurizers ran locally. Current framework versions alone do not establish DGL binary ABI compatibility. Use the DGL platform matrix and validate the chosen environment before expensive training.
No TensorFlow/JAX backend, public pretrained weight download, full MoleculeNet run, full GAN training, CIF/materials model training or ProtBERT training was executed. Those workflows are bounded, source-verified templates. No GPU or scientific performance claims follow from this CPU review.
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