DeepPurpose#

This skill is adapted from DeepPurpose, copyright (c) 2020 Kexin Huang, Tianfan Fu, licensed under BSD 3-Clause.

Prefer a local DeepPurpose checkout over web summaries. Treat a directory as the repo root when it contains setup.py, requirements.txt, DeepPurpose/, DEMO/, and toy_data/.

Workflow#

1. Classify the request: environment/install, task pipeline, dataset format, pretrained model, notebook/demo adaptation, or troubleshooting.
2. Read only the relevant reference file:
   • installation, dependency sanity, or smoke tests: references/install-and-dependencies.md
   • task/module selection, encodings, splits, and core APIs: references/tasks-and-entrypoints.md
   • dataset loaders, custom text formats, pretrained downloads, and result outputs: references/data-and-pretrained.md
3. Verify advice against local files before answering. Prefer README.md, DeepPurpose/utils.py, DeepPurpose/dataset.py, and the task module the user actually needs.
4. Reuse the upstream API shape instead of inventing wrappers. The maintained paths are:
   • DTI: DeepPurpose/DTI.py
   • compound property prediction: DeepPurpose/CompoundPred.py
   • DDI: DeepPurpose/DDI.py
   • PPI: DeepPurpose/PPI.py
   • protein function prediction: DeepPurpose/ProteinPred.py
   • one-line repurposing and virtual screening: DeepPurpose/oneliner.py
5. Prefer the closest notebook in DEMO/ when the user wants an example or a starting point.

Execution Rules#

• Build datasets with DeepPurpose.dataset helpers or local text files in the expected format.
• Encode and split with data_process(...), then build a config with generate_config(...), then call model_initialize(**config) or model_pretrained(...).
• Keep the task/module aligned:
  • DTI uses both drug and target inputs
  • compound property uses drug-only inputs
  • DDI uses X_drug plus X_drug_
  • PPI uses X_target plus X_target_
  • protein function uses target-only inputs
• For repurposing or screening, prefer the existing helpers: DTI.repurpose, DTI.virtual_screening, CompoundPred.repurpose, and oneliner.repurpose or oneliner.virtual_screening.
• Warn when a step triggers network downloads. Dataset helpers and pretrained model helpers fetch remote files.
• Distinguish static validation from runtime validation. DeepPurpose/utils.py imports heavy dependencies immediately, so a real import needs RDKit, PyTorch, Descriptastorus, and related packages installed first.

Source Files#

Use these local files as the primary source of truth when present:

• README.md
• requirements.txt
• environment.yml
• setup.py
• DeepPurpose/utils.py
• DeepPurpose/dataset.py
• DeepPurpose/oneliner.py
• DeepPurpose/DTI.py
• DeepPurpose/CompoundPred.py
• DeepPurpose/DDI.py
• DeepPurpose/PPI.py
• DeepPurpose/ProteinPred.py
• toy_data/
• DEMO/