Skill: zoeprior/deeppurpose (version 1.0.1)

--- 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 --- # 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/`


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