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预测化合物的admet性质
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | admet-predict |
| description | 预测化合物的admet性质 |
| license | Proprietary. LICENSE.txt has complete terms |
When given a compound, predict its ADMET properties (absorption, distribution, metabolism, excretion, and toxicity).
| 参数名 | 类型 | 必填 | 描述 |
|---|---|---|---|
smiles_file | string | 是 | 包含smiles的文件 文件格式可能是.csv或者.txt |
output_dir | string | 是 | 文件输出的目录 |
source "$(conda info --base)/etc/profile.d/conda.sh" && conda activate huntianling && CUDA_VISIBLE_DEVICES=-1 python admet-predict.py --smiles_file {{smiles_file}} --output_dir {{output_dir}}
Use this skill when user wants to view one or multiple PDB / SDF files in the 3D structure viewer. Trigger when user mentions: open pdb, open sdf, 3D structure, view protein, view molecule, or provides local .pdb/.sdf file(s) to view. Supports viewing multiple files simultaneously by chaining path parameters with '&'.
When the service is deployed on a cloud server, use this skill when the user provide a file path and ask to download to their personal computer. This skill upload a file to Alibaba Cloud OSS and generate a shareable preview link for users to download easily. Trigger especially when user mentions: download file, upload to OSS, cloud storage upload, view image, preview image, generate share link, preview file via URL, or provides a local file path that needs to be shared online.
输入PDB ID，获取相应的PDB文件
Build the final collection report in two steps: model writes a report template, then script injects each paper's Brief Conclusion and abs URL from summary.md by arXiv-id placeholders.
Tool-only paper processing skill with a manual language parameter: supports batch artifact download for many papers or single-paper download, then the model manually reads source/PDF and writes summary.md in the selected language. Use when per-paper comprehension should be model-driven instead of script-generated.
Model-driven arXiv retrieval workflow for building a paper set with a manual language parameter: initialize a run, fetch metadata for each model-designed query, let the model filter irrelevant items per query by keep indexes, then merge and dedupe into per-paper metadata directories. Use when query planning and relevance filtering should be done by the model, not rule-based heuristics.