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预测人类肝脏微小细胞的清除与奇拉性聚焦的图形神经网络
Chengtao Pu1, Lingxi Gu1, Yuxuan Hu2
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
Journal of chemical information and modeling
|July 8, 2024
概括
预测药物清除是非常重要的. 这项研究表明,像TetraDMPNN这样的奇拉性感知图形神经网络,可以使用人类肝脏显微体数据改善预测,优于其他模型.
科学领域:
- 药理动力学和药物新陈代谢
- 计算化学计算化学
- 机器学习在药物发现中的作用
背景情况:
- 药物清除率是药物候选设计中的一个关键参数.
- 机器学习模型可以预测药物清除,但经常面临数据可靠性问题,忽视分子性.
- 现有的in silico定量结构-属性关系模型往往忽视了奇拉性的影响.
研究的目的:
- 评估机器学习模型的性能,包括以奇拉性为重点的图形神经网络 (GNN),用于预测人肝显微体 (HLM) 药物清除.
- 使用精心策划的HLM数据集,比较不同GNN架构和基线模型的预测能力.
- 调查分子性对药物清除预测准确性的影响.
主要方法:
- 从公共HLM数据中策划了两个不同的数据集,质量和数量各不相同.
- 开发和评估了两个基线模型 (随机森林和深度神经网络).
- 提出并测试了三个以度为重点的GNN:DMPNN,TetraDMPNN和ChIRo,评估它们在HLM数据上的表现.
- 使用分子相似性方法定义了模型的适用性领域.
主要成果:
- 采用TetraDMPNN模型,将二维结构的性结合起来,实现了最佳的性能.
- TetraDMPNN的测试R平方值为0.639,测试根平均平方值的误差为0.429.
- 与基线模型相比,具有性意识的GNN显示出更高的性能.
结论:
- 捕捉分子性性的图形神经网络为实际的药物清除预测提供了显著的潜力.
- 通过有效利用性信息,TetraDMPNN模型代表了预测HLM清除的有希望的进步.
- 使用先进的计算方法准确预测药物清除对于有效的药物开发至关重要.
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