什么是有效的,什么不是,为什么? 对吸收模型的工业视角
Pierre Llompart1,2, Claire Minoletti2, Gilles Marcou1
1Laboratory of Cheminformatics, UMR7140, University of Strasbourg, 67000 Strasbourg, France.
Journal of medicinal chemistry
|July 14, 2025
概括
药物发现面临着吸收不良和恢复低的挑战. 这项研究模拟有机分子的吸收,强调恢复和物理化学特性,用于多参数优化 (MPO),以提高药物开发效率.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药理动力学 药理动力学
背景情况:
- 在药物发现中,优化失败通常归因于吸收不良,排泄物运输和低恢复率.
- 现有的模型往往缺乏对工业化学空间的全面分析和运输路线的表征.
研究的目的:
- 使用公共和工业数据开发一个全面的模型来预测有机分子的吸收.
- 确定影响多参数优化 (MPO) 的关键参数,提高药物发现效率.
主要方法:
- 关于有机分子吸收的公共和工业数据的全面建模.
- 工业化工空间的比较分析,以检查透性参数.
- 多任务学习方法用于模型开发和验证.
主要成果:
- 在MPO中证明了回收,分布系数和拓极地表面积的关键重要性.
- 由于协议变异,公众和工业数据之间的差异被揭示出来,突出了对标准化测量的需求.
- 在工业化工空间的运输路线表征中发现了误解.
结论:
- 工业数据对于避免适用性领域问题和确保预测建模中的测量标准化至关重要.
- 将预测模型与生成的地形绘图相结合,为化学空间探索和优化提供了一个视觉策略.
- 拟议的方法支持MPO,旨在提高药物发现的整体效率.
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