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deeppurpose

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Help install, inspect, run, troubleshoot, and adapt the DeepPurpose molecular modeling library for drug-target interaction prediction, compound property prediction, DDI, PPI, protein function prediction, drug repurposing, and virtual screening. Use when the user mentions DeepPurpose, `from DeepPurpose import`, `DTI`, `CompoundPred`, `DDI`, `PPI`, `ProteinPred`, `oneliner`, `data_process`, `generate_config`, DeepPurpose datasets, encodings, pretrained models, toy data, or demo notebooks.

Install

openclaw skills install @zoeprior/deeppurpose

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/