一组化合物-标对数据集:药物,临床候选物和其他生物活性化合物之间的差异
A Lina Heinzke1, Barbara Zdrazil1, Paul D Leeson2
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom.
Scientific data
|October 21, 2024
概括
这项研究引入了来自ChEMBL的新型,开源的化合物标相互作用数据集,有助于药物发现研究. 该数据集有助于分析候选药物和已知的药物,旨在降低消耗率.
科学领域:
- 药物的发现和开发.
- 药品化学 药品化学 是一个
- 生物信息学是一种生物信息学.
背景情况:
- 药物发现中的高消耗率需要更好的预测模型.
- 了解活性化合物,临床候选物和药物之间的差异至关重要.
- 现有的药物发现数据集通常是专有的,无法公开访问.
研究的目的:
- 创建一个高质量的,复合目标对的开源数据集.
- 具体说明涉及药物和临床候选药物的相互作用.
- 促进进行比较分析,以改善药物候选药物选择.
主要方法:
- 从ChEMBL释放32中提取化合物目标对.
- 包括具有测量生物活性或已知的相互作用的对.
- 自动化和可重复的数据提取过程.
- 已知药物标和临床候选药物标相互作用的具体注释.
主要成果:
- 创建了一个数据集,包含614,594个化合物-目标对.
- 包括已知的5109种药物向相互作用.
- 包括3932个已知的临床候选-目标相互作用.
- 数据提取方法是完全可重现的.
结论:
- 引入的数据集为药物发现研究提供了宝贵的资源.
- 便于分析区分成功候选药物与其他活性化合物的因素.
- 对数据和代码的开放访问促进了可重复性和进一步的研究.
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