deeppurpose

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name: deeppurpose description: 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. license: BSD-3-Clause

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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/