化学空间网络的理解显示了关键性的迹象
Nicola Amoroso1,2, Nicola Gambacorta3,4, Fabrizio Mastrolorito3
1Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy. nicola.amoroso@uniba.it.
Scientific reports
|December 4, 2023
概括
化学空间网络 (CSN) 为分析发育毒性 (Dev Tox) 的传统方法提供了强大的替代方案. 这项研究揭示了CSN在化学数据中发现复杂的模式,有助于识别药物发现的有毒化合物.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 药物发现 药物发现
背景情况:
- 化学空间建模对于药物发现中的预测毒理学至关重要.
- 传统的分子描述器面临着维度的诅咒.
- 复杂的网络提供了替代的代表性,避免了基于坐标的缺点.
研究的目的:
- 使用化学空间网络 (CSN) 分析发育毒性 (Dev Tox).
- 为了解决收集可靠的Dev Tox数据所面临的挑战.
- 探索CSN对发育毒性的预测潜力.
主要方法:
- 专门用于发育毒性数据的化学空间网络 (CSN) 的构建和分析.
- 检查网络组织和属性.
- 在CSN中的相位过渡中识别化学相似性和毒性素.
主要成果:
- 发展性毒性CSN表现出一个复杂的,非随机的组织.
- 分析揭示了成熟的毒性体,包括基衍生物,水素,巴比图酸盐,氨基醇,类固醇和以太类化学物质.
- 这些已识别的化学特征作为有效的警报,在发展性毒性评估中优先考虑化学品.
结论:
- 化学空间网络为发展性毒性提供了宝贵的见解.
- 开发毒素CSN的非随机结构有助于识别潜在的危险.
- 可以将CSN作为预测工具来优先测试化学品,改善母婴健康保护.
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