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开发一个阅读交叉衍生分类模型,用于预测突变性数据,并与传统的QSAR模型和专家系统进行比较

Sapna Kumari Pandey¹, Kunal Roy¹

¹Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.

|November 22, 2023

概括

这项研究开发了一种新的基于阅读的定量结构-活性关系 (QSAR) 模型,用于预测化学变异性,比传统方法提供更高的准确性和效率. 新模型加强了对新候选化学品的安全评估,减少了对动物试验的依赖.

科学领域:

毒理学和化学信息学
计算毒理学计算毒理学
药物发现和开发药物发现和开发

背景情况:

变异性评估对于监管,环境和医疗安全至关重要.
动物对变异性进行生物测试是昂贵且耗时的.
现有的定量结构-活性关系 (QSAR) 模型的性能可能会随着新的化学实体而下降.

研究的目的:

开发和验证一个改进的QSAR模型来预测突变性.
探索基于阅读交叉 (RA) 功能的QSAR建模的实用性.
将基于RA的QSAR与传统的QSAR和现有工具的性能进行比较.

主要方法:

开发了一种传统的线性差异分析 (LDA) QSAR模型,使用Ames数据集 (6512个化合物) 和2D描述器.
创建了一个基于RA的新型LDA QSAR模型,利用相似性衍生特征.
通过使用外部组合216种化合物的模型进行验证,并将性能与经合组织工具箱进行比较.

主要成果:

与经典的LDA QSAR模型相比,基于RA的LDA QSAR模型显示出更好的预测性,可转移性和可解释性.
基于RA的模型需要显著减少描述符.
源自RA的模型在外部验证集上显示出有希望的性能.

结论:

基于阅读交叉的QSAR建模为预测变异性提供了更有效的方法.
这种方法可以补充现有的专家系统,并减少对动物试验的需求.
开发的基于RA的模型为选候选化学品提供了有价值的工具.

关键词:

在 LDA LDA 中.机器学习机器学习突变性突变性突变性经合组织工具箱在QSAR中使用QSAR.阅读横向的阅读方式

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