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.

1. 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

1. Verify advice against local files before answering. Prefer README.md,

DeepPurpose/utils.py, DeepPurpose/dataset.py, and the task module the user

actually needs.

1. 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

1. 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/