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预测毒理学中的分子相似性,重点是q-RASAR技术

Arkaprava Banerjee¹, Kunal Roy²

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

Methods in molecular biology (Clifton, N.J.)

|September 23, 2024

概括

阅读交叉 (RA) 和定量阅读交叉 (qRA) 使用相似性预测化合物特性. 一种新的定量阅读跨结构-活性关系 (q-RASAR) 方法提高了预测毒理学的解释性和模型可靠性.

科学领域:

计算化学计算化学
预测性毒理学预测性毒理学
化学信息学化学信息学

背景情况:

类似性原理是in silico预测方法的基础,假设类似的化合物表现出类似的特性.
阅读交叉 (RA) 是一种流行的基于相似性的方法,用于填补数据缺口,特别是在监管环境中,因为它不需要像QSAR这样的大数据集.
定量阅读交叉 (qRA) 引入了监督权重,以实现更精确的预测,但特征的解释性仍然是一个挑战.

研究的目的:

介绍和讨论新的定量阅读跨结构-活动关系 (q-RASAR) 方法.
为了解决现有的阅读交叉方法的解释性限制.
提出一种用于生成统计可靠和可解释的预测模型的方法.

主要方法:

该研究讨论了阅读交叉 (RA) 和定量阅读交叉 (qRA) 的基本概念.
它详细介绍了定量阅读跨结构-活动关系 (q-RASAR) 方法的发展.
q-RASAR结合了QSAR和RA原则,利用相似性和基于错误的描述符来生成模型.

主要成果:

q-RASAR方法产生了统计学上可靠和预测模型.
这些模型是简单的和可解释的,与以前的qRA方法不同.
q-RASAR有助于识别源和查询组合的基本特征和性质.

结论:

定量阅读交叉结构-活动关系 (q-RASAR) 为传统的阅读交叉方法提供了一个可解释的替代方案.
这种方法提高了在毒理学和监管科学中基于相似性的预测的实用性.
该章节回顾了RA,qRA,q-RASAR以及它们应用的可用工具.

关键词:

班纳吉 - 罗伊系数分子相似性分子相似性预测预测预测阅读横向的阅读方式在 q-RASAR 中使用.

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