人工智能应用于染色体学中的定量结构-保留关系计算
Jingru Xie1,2,3, Si Chen1,3, Liang Zhao1,2,3
1School of Medicine, Shanghai University, Shanghai, 200444, China.
Journal of pharmaceutical analysis
|February 3, 2025
概括
定量结构-保留关系 (QSRR) 模型预测了色谱学中的分析物保留时间. 本综述探讨了QSRR工作流程,AI应用以及染色体分析的未来方向.
科学领域:
- 分析化学 分析化学
- 化学信息学 化学信息学
背景情况:
- 定量结构-保留关系 (QSRR) 对于色谱分离分析至关重要.
- QSRR将分子结构与保留行为相关联,有助于方法开发和化合物识别.
研究的目的:
- 提供QSRR工作流程和应用程序的全面审查.
- 突出人工智能 (AI) 在QSRR中的作用,这是一个尚未探索的领域.
- 讨论目前保留时间 (RT) 预测的局限性,并提出解决方案.
主要方法:
- 对QSRR方法学的现有文献的审查.
- 分析QSRR模型开发和验证过程.
- 专注于在QSRR框架内整合AI技术.
主要成果:
- QSRR模型有效地预测保留时间 (RT),加速色谱方法的开发.
- 人工智能集成提供了新的方法来提高QSRR模型的准确性和范围.
- 在当前的RT预测中发现的局限性需要进一步的研究和创新策略.
结论:
- QSRR是理解染色分离和支持药物发现的强大工具.
- 未来的QSRR研究应该专注于用于更广泛应用的AI驱动的创新.
- 开发先进的QSRR策略将增强色谱分析和化合物表征.
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