pampa_ncats

## Dataset Details

### Dataset Description

PAMPA (parallel artificial membrane permeability assay) is a commonly
employed assay to evaluate drug permeability across the cellular membrane.
PAMPA is a non-cell-based, low-cost and high-throughput alternative to cellular models.
Although PAMPA does not model active and efflux transporters, it still provides permeability values
that are useful for absorption prediction because the majority of drugs are absorbed
by passive diffusion through the membrane.

- **Curated by:**
- **License:** CC BY 4.0

### Dataset Sources

- [original dataset link](https://tdcommons.ai/single_pred_tasks/adme/#pampa-permeability-ncats)
- [corresponding publication](https://journals.sagepub.com/doi/full/10.1177/24725552211017520)

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@article{siramshetty2021validating,
title={Validating ADME QSAR Models Using Marketed Drugs},
author={Siramshetty, Vishal and Williams, Jordan and Nguyen, DHac-Trung and Neyra, Jorge and Southall,
Noel and Math'e, Ewy and Xu, Xin and Shah, Pranav},
journal={SLAS DISCOVERY: Advancing the Science of Drug Discovery},
volume={26},
number={10},
pages={1326--1336},
year={2021},
publisher={SAGE Publications Sage CA: Los Angeles, CA}
}
```

## 数据集详情

### 数据集描述
PAMPA（parallel artificial membrane permeability assay，平行人工膜渗透性试验）是一种常用于评估药物跨细胞膜渗透性的实验检测方法。相较于传统细胞模型，PAMPA是一种非细胞依赖、低成本且高通量的替代方案。尽管PAMPA无法模拟主动转运体与外排转运体，但它仍可提供可用于药物吸收预测的渗透性数值，因为绝大多数药物均通过被动扩散方式透过细胞膜被机体吸收。

- **整理方：**
- **许可协议：** CC BY 4.0

### 数据集来源
- [原始数据集链接](https://tdcommons.ai/single_pred_tasks/adme/#pampa-permeability-ncats)
- [对应发表论文](https://journals.sagepub.com/doi/full/10.1177/24725552211017520)

## 引用
<!-- 若存在介绍该数据集的论文或博客文章，需在此处补充其APA与BibTeX格式的引用信息。 -->

**BibTeX：**
bibtex
@article{siramshetty2021validating,
title={基于上市药物验证ADME（Absorption, Distribution, Metabolism, Excretion，吸收-分布-代谢-排泄）QSAR（Quantitative Structure-Activity Relationship，定量结构-活性关系）模型},
author={Siramshetty, Vishal and Williams, Jordan and Nguyen, DHac-Trung and Neyra, Jorge and Southall,
Noel and Math'e, Ewy and Xu, Xin and Shah, Pranav},
journal={SLAS Discovery：推进药物发现科学},
volume={26},
number={10},
pages={1326--1336},
year={2021},
publisher={SAGE Publications Sage CA，美国加利福尼亚州洛杉矶市}
}