开发用于使用学习到等级 (LTR) 技术进行高通量ADME分析的预测多重反应监测 (MRM) 模型
Ramon Adalia1,2, Shivani Patel3, Anthony Paiva3
1Lead Molecular Design S.L., 08172 Sant Cugat de Valles, Spain.
一个新的学习到等级模型预测了用于多重反应监测 (MRM) 质谱的产品离子,简化了药物发现. 这种计算方法消除了广泛的实验优化,加速了复杂生物样本中的化合物分析.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 多重反应监测 (MRM) 是药物发现中的一个关键技术,用于量化化合物.
- 实验优化MRM条件,包括产品离子选择,是耗时和资源密集的.
- 通过计算预测产品离子可以显著加速药物开发管道.
研究的目的:
- 开发一个学习到等级 (LTR) 模型,直接从化合物结构中预测产品离子.
- 消除在药物发现工作流程中对实验MRM优化的需求.
- 评估模型的性能及其对高通量生物分析的影响.
主要方法:
- 使用了5757个实验确定MRM条件的数据集.
- 使用MassChemSite软件生成理论碎片和质量电荷比.
- 经过训练和验证的LTR模型使用NDCG@k和Coverage@k等指标.
主要成果:
- 在一个验证集上,LTR模型实现了高性能 (NDCG@5 = 0.732,Coverage@5 = 0.841).
- 来自该模型的预测应用于高通量Caco-2透性和代谢稳定性测试.
- 与实验MRM条件相比,该模型的预测导致97%的生物等效结果.
结论:
- 开发的LTR模型有效地预测了MRM分析的产品离子,减少了实验工作量.
- 这种计算方法为加速药物发现和开发提供了重大进展.
- 该模型在生物测试中的成功应用证明了其在定量质谱学中的实际实用性.
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