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数据与复现
TO
TorchDrug
维护者 K-Dense Inc. · 最近更新 2026年4月1日
TorchDrug is a comprehensive PyTorch-based machine learning toolbox for drug discovery and molecular science. Apply graph neural networks, pre-trained models, and task definitions to molecules, proteins, and biological knowledge graphs, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis planning, with 40+ curated datasets and 20+ model architect….
原始来源
K-Dense-AI/claude-scientific-skills
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/torchdrug
- 维护者
- K-Dense Inc.
- 许可
- Apache-2.0 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
- TorchDrug是一个comprehensive PyTorch-based 机器学习 toolbox ，用于 drug discovery 、 molecular science. Apply graph neural networks，pre-trained models，、 task definitions to molecules，proteins，、 biological knowledge graphs，，涵盖 molecular property prediction，protein modeling，knowledge graph reasoning，molecular generation，retrosynthesis planning，，支持 40+ curated 数据集s 、 20+ model architectures。
- uv pip install torchdrug[full]。
原始文档
SKILL.md 摘录
When to Use This Skill
This skill should be used when working with:
Data Types:
- SMILES strings or molecular structures
- Protein sequences or 3D structures (PDB files)
- Chemical reactions and retrosynthesis
- Biomedical knowledge graphs
- Drug discovery datasets
Tasks:
- Predicting molecular properties (solubility, toxicity, activity)
- Protein function or structure prediction
- Drug-target binding prediction
- Generating new molecular structures
- Planning chemical synthesis routes
- Link prediction in biomedical knowledge bases
- Training graph neural networks on scientific data
Libraries and Integration:
- TorchDrug is the primary library
- Often used with RDKit for cheminformatics
- Compatible with PyTorch and PyTorch Lightning
- Integrates with AlphaFold and ESM for proteins
Quick Example
from torchdrug import datasets, models, tasks
from torch.utils.data import DataLoader
## Load molecular dataset
dataset = datasets.BBBP("~/molecule-datasets/")
train_set, valid_set, test_set = dataset.split()
适用场景
- SMILES strings 或 molecular structures。
- Protein sequences 或 3D structures (PDB files)。
- Chemical reactions 、 retrosynthesis。
- Biomedical knowledge graphs。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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