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deeppurpose

Name: deeppurpose
Author: zoeprior

作者 zoeprior · GitHub ↗ · v1.0.1 · MIT-0

cross-platform ⚠ suspicious

113

总下载

当前安装

版本数

在 OpenClaw 中安装

/install deeppurpose

功能描述

Help install, inspect, run, troubleshoot, and adapt the DeepPurpose molecular modeling library for drug-target interaction prediction, compound property pred...

使用说明 (SKILL.md)

DeepPurpose

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

Classify the request: environment/install, task pipeline, dataset format, pretrained model, notebook/demo adaptation, or troubleshooting.
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
Verify advice against local files before answering. Prefer README.md, DeepPurpose/utils.py, DeepPurpose/dataset.py, and the task module the user actually needs.
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
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/

安全使用建议

This skill is an instruction-only advisor for the DeepPurpose repo and is internally consistent, but exercise normal caution: 1) Inspect the repository yourself before running code; the SKILL.md expects you to read local files and prefer a local checkout. 2) Do static checks first (python3 setup.py --name, compileall) — importing modules will trigger heavy native dependencies and may fail or perform network downloads. 3) Run any suggested installs or model downloads in an isolated environment (conda env / container) to avoid contaminating your system. 4) When a workflow would download pretrained assets or datasets, prefer providing local pretrained_dir or explicit permission before allowing automatic downloads. 5) Because the skill source is 'unknown' and there are remote-download behaviors documented (Harvard Dataverse for pretrained assets and BindingDB fallbacks), consider auditing any downloaded artifacts and running in a network-restricted sandbox if you have sensitive data or credentials on the same machine.

功能分析

Type: OpenClaw Skill Name: deeppurpose Version: 1.0.1 The skill bundle facilitates the use of the DeepPurpose library, which involves high-risk capabilities such as executing shell commands for installation and validation (e.g., pip, conda, and setup.py in references/install-and-dependencies.md) and performing network downloads for datasets and pretrained models from external sources like Harvard Dataverse and BindingDB (references/data-and-pretrained.md). While these actions are aligned with the stated purpose of the library and the instructions in SKILL.md include a requirement to warn the user before network activity, the inherent risks of automated shell execution and remote content retrieval meet the threshold for a suspicious classification.

能力评估

✓ Purpose & Capability

The name/description match the SKILL.md: guidance focuses on installing, inspecting, running, and adapting DeepPurpose. The files referenced and workflows described (data_loaders, encodings, model entrypoints, pretrained downloads) are appropriate for a DeepPurpose support skill.

✓ Instruction Scope

Runtime instructions direct the agent to read local repository files (README, DeepPurpose/*.py, DEMO/, toy_data/) and to prefer static validation before runtime imports. These file reads are directly relevant to the stated purpose. The SKILL.md explicitly warns about network downloads triggered by dataset and pretrained model helpers.

✓ Install Mechanism

There is no install spec and no code files to execute as part of skill installation (instruction-only). That minimizes disk-write and remote-install risk. The skill documents upstream install commands (conda/pip) for the user, which is expected and reasonable.

✓ Credentials

The skill declares no required environment variables, credentials, or config paths. The heavy dependencies (RDKit, PyTorch, Descriptastorus, DGL, etc.) are documented and expected for this kind of library; they are proportional to the described tasks. The SKILL.md also cautions about downloads performed by helper functions.

✓ Persistence & Privilege

always is false and the skill is user-invocable. There is no request to modify other skills or system-wide settings. The guidance to operate on local repo files is normal for a repo-support skill and does not imply elevated privileges.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install deeppurpose
安装完成后，直接呼叫该 Skill 的名称或使用 /deeppurpose 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.1

- Added BSD-3-Clause license to skill metadata. - Included explicit upstream copyright and license notice. - No changes to workflows or core guidance.

v1.0.0

- Initial release of the DeepPurpose skill. - Assists with installing, using, adapting, and troubleshooting the DeepPurpose molecular modeling library. - Supports workflows for drug-target interaction, compound property, DDI, PPI, protein function prediction, drug repurposing, and virtual screening. - Provides guidance on dataset formats, pretrained models, demos, and encoding pipelines. - Ensures answers reference local files and original APIs for accurate guidance.

元数据

Slug deeppurpose

版本 1.0.1

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 2

常见问题

deeppurpose 是什么？

Help install, inspect, run, troubleshoot, and adapt the DeepPurpose molecular modeling library for drug-target interaction prediction, compound property pred... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 113 次。

如何安装 deeppurpose？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install deeppurpose」即可一键安装，无需额外配置。

deeppurpose 是免费的吗？

是的，deeppurpose 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

deeppurpose 支持哪些平台？

deeppurpose 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 deeppurpose？

由 zoeprior（@zoeprior）开发并维护，当前版本 v1.0.1。