探索三维分子表示在Caco-2透性预测中的有效性
1School of Chemistry, Chemistry Engineering and Biotechnology, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
Journal of chemical information and modeling
|October 13, 2025
概括
三维神经网络 (3D-NN) 通过提取关键分子特征来改善药物吸收预测,优于传统方法. 需要进一步的数据来充分评估载体介导透性的性能.
科学领域:
- 药理动力学和药物新陈代谢
- 计算化学计算化学
- 生物物理化学 生物物理化学
背景情况:
- 可可-2单层测试是药物吸收预测的标准.
- 当前的方法通常依赖于2D表示和预定义的特征,可能缺少关键的3D结构洞察力.
- 需要先进的预测模型,包括3D分子信息.
研究的目的:
- 评估3D神经网络 (3D-NN) 与传统的1D,2D和基于特征的模型的性能,以预测药物透性.
- 评估3D-NN在不同化学架构中的通用性,并与现有的文献模型进行比较.
- 探索3D-NN嵌入的实用性,以区分低透性和高透性化合物.
主要方法:
- 在合并的文献数据集上对各种模型 (3D-NN,1D,2D,预定义特征) 进行基准测试.
- 使用基于支架的分割来对模型通用性进行可靠的评估.
- 将模型性能与已建立的文献模型和外部测试集进行比较,包括载体介导化合物.
主要成果:
- 3D-NN模型独立地提取了用于透性预测的更相关特征,证明了跨支架的优越通用性.
- 3D-NN性能与专门的文献模型相比具有竞争力.
- 来自3D-NN的嵌入显示了低透性和高透性化合物之间的明显分离,表明了未来应用的潜力.
- 由于数据的局限性,对传送器介导的透性进行的分析无法得出结论.
结论:
- 3D分子表示为使用Caco-2试验预测药物透性提供了显著的优势.
- 3D-NN模型显示了改善药物吸收预测的前景,特别是使用更大的数据集.
- 需要对更全面的数据集进行进一步的研究,以充分评估3D-NN在预测传送器介导透性的性能.
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