AG
AgentD autonomous drug discovery: target identification, hit finding, ADMET optimization.
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数据与复现
PY
PyTDC
维护者 K-Dense Inc. · 最近更新 2026年4月1日
PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery and development. Access curated datasets spanning the entire therapeutics pipeline with standardi.
原始来源
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pytdc
- 维护者
- K-Dense Inc.
- 许可
- MIT license
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- PyTDC是一个open-science 平台 providing AI-ready 数据集s 、 基准评测 ，用于 drug discovery 、 development. Access curated 数据集s spanning entire therapeutics pipeline ，支持 standardized evaluation metrics 、 meaningful data splits，organized into three categories：single-instance prediction (molecular/protein properties)，multi-instance prediction (drug-target interactions，DDI)，、 generation (molecule generation，retrosynthesis)。
- Caco2 - Intestinal permeability。
- HIA - Human intestinal absorption。
- Bioavailability - Oral bioavailability。
- Lipophilicity - Octanol-water partition coefficient。
原始文档
SKILL.md 摘录
When to Use This Skill
This skill should be used when:
- Working with drug discovery or therapeutic ML datasets
- Benchmarking machine learning models on standardized pharmaceutical tasks
- Predicting molecular properties (ADME, toxicity, bioactivity)
- Predicting drug-target or drug-drug interactions
- Generating novel molecules with desired properties
- Accessing curated datasets with proper train/test splits (scaffold, cold-split)
- Using molecular oracles for property optimization
Installation & Setup
Install PyTDC using pip:
To upgrade to the latest version:
Core dependencies (automatically installed):
- numpy, pandas, tqdm, seaborn, scikit_learn, fuzzywuzzy
Additional packages are installed automatically as needed for specific features.
Quick Start
The basic pattern for accessing any TDC dataset follows this structure:
Where:
<problem>: One ofsingle_pred,multi_pred, orgeneration<Task>: Specific task category (e.g., ADME, DTI, MolGen)<Dataset>: Dataset name within that task
Example - Loading ADME data:
from tdc.single_pred import ADME
data = ADME(name='Caco2_Wang')
split = data.get_split(method='scaffold')
适用场景
- Working ，支持 drug discovery 或 therapeutic ML 数据集s。
- 评测 机器学习 models on standardi。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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