定量结构 通过神经网络进行药物发现或监管使用的活性/毒性关系
1Theory Department, National Institute of Chemistry, Ljubljana, Slovenia.
Current topics in medicinal chemistry
|October 23, 2023
概括
定量结构-活动关系 (QSAR) 建模利用机器学习进行化学数据分析. 本综述强调了QSAR原则,数据策划和人工神经网络,用于预测生物特性.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 定量结构-活性关系 (QSAR) 建模在药物发现和监管科学中至关重要.
- 数据收集和机器学习 (ML) 的最新进展增强了QSAR的能力.
- 人工智能 (AI) 工具的整合正在改变数据驱动的建模方法.
结论:
- QSAR建模,特别是ANN,为化学-生物相互作用提供了强大的洞察力.
- 数据驱动的方法对于推进预测毒理学和药物设计至关重要.
- 审查强调了强大的QSAR模型开发和验证的重要性.
关键词:
人工神经网络的人工神经网络分类器分类器分类器分类器分类器对抗传播的方法深度学习是一种深度学习.药物开发 药物开发分子描述器分子描述器预测模型的预测模型.在QSAR中使用QSAR.毒性评估. 有毒性的评估.更多相关视频
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