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化学信息学和预测毒性建模中的分子相似性:从定量阅读交叉 (q-RA) 到定量阅读交叉结构-活性关系 (q-RASAR) 使用机器学习

Arkaprava Banerjee¹, Supratik Kar², Kunal Roy¹

¹Department of Pharmaceutical Technology, Drug Theoretics and Cheminformatics (DTC) Laboratory, Jadavpur University, Kolkata, India.

Critical reviews in toxicology

|September 3, 2024

概括

本综述涵盖了分子相似性,重点关注跨读 (RA) 和跨读结构-活性关系 (RASAR). 这些方法有助于预测建模,并填补化学性质的数据缺口,包括毒性评估.

科学领域:

化学信息学化学信息学
计算化学是一种计算化学.
毒理学毒理学毒理学

背景情况:

分子相似性对于化学中的预测建模至关重要.
诸如定量结构-活性关系 (QSAR) 和交叉读取 (RA) 等计算工具对于填补数据空白至关重要,特别是对于监管环境中的毒性终点.
该综述追踪了RA的演变,从早期的结构信息使用到纳入物理化学,ADME和生物相似性背景.

研究的目的:

提供化学信息学和预测建模中分子相似性的全面审查.
批判性地检查阅读交叉 (RA) 和阅读交叉结构-活动关系 (RASAR) 方法.
突出最近的进展和新型模型,将RA与QSAR集成在一起.

主要方法:

关于分子相似性概念和应用的文献综述.
专注于阅读交叉 (RA) 方法及其发展.
探索像ToxRead,GenRA和q-RASAR这样的集成模型.

主要成果:

分子相似性,特别是RA和RASAR,是用于预测建模的强大方法.
RA有效地解决了各种化学性质的数据缺口,包括毒性.
最近,RA与QSAR的整合导致了复杂的预测模型.

结论:

阅读和RASAR是化学信息学中必要的工具,用于填补数据缺口和监管科学.
关节炎的演变,包括各种相似性背景,增强了其预测能力.
新的综合模型代表了计算毒理学和财产预测方面的重大进步.

关键词:

分子相似性分子相似性在QSAR中使用QSAR.在这里,我们可以看到RASAR RASAR.预测性毒理学预测性毒理学阅读横向的阅读.

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