基于液体染色学的QSRR模型对药物相关的小分子的最新应用:一篇综述
Md Sajjadur Rahman1, Md Kamrul Hasan Khan2, Jane Kawakami1
1Analytical Research & Development, Pfizer Inc., Eastern Point Road, Groton, CT 06340, USA.
Journal of pharmaceutical sciences
|November 1, 2025
概括
量化结构保留关系 (QSRR) 模型正在超越保留时间预测,以涵盖更广泛的应用. 转移学习和遵守经合组织指导方针提高了QSRR模型可靠性和性能,用于各种化学数据集.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 量化结构保留关系 (QSRR) 模型越来越多地被用于超出简单的保留时间 (RT) 预测.
- 现在的应用包括物理化学性质选,多目标性质预测和分离机制的阐明.
- 新兴趋势包括将设计时分析质量 (AQbD) 与QSRR和实验设计 (DoE) 整合起来,以进行可靠的方法验证.
研究的目的:
- 探索QSRR模型在分析化学中的高级应用.
- 突出转移学习的潜力,以改善有限数据集的QSRR模型准确性.
- 强调遵守经合组织关于可靠QSRR模型开发的指导方针的重要性.
主要方法:
- 通过在大型数据库 (如METLIN-SMRT,CMRT) 上预训练模型,并对特定数据集进行微调,利用转移学习.
- 应用经合组织 (Q) SAR指南用于模型开发,确保透明度,定义终点和适用性领域.
- 采用3D描述器和图形神经网络 (GNN) 模型进行立体异构体分析.
主要成果:
- 转移学习显示了提高QSRR模型准确性的前景,特别是在较小的,基于项目的数据集.
- 遵守经合组织原则可以加强模型的可靠性和可重复性.
- 仔细选择分子描述符并确保结构多样性对于可靠的预测至关重要.
结论:
- QSRR建模正在向化学分析和药物发现中更全面的应用发展.
- 转移学习提供了一种强大的策略,可以克服QSRR中的数据限制.
- 整合机械解释性和绿色分析化学原则可确保可持续和高效的QSRR应用.
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