PKSmart:一个开源的计算模型,用于预测小分子静脉注射药理学
Srijit Seal1,2, Maria-Anna Trapotsi3, Manas Mahale4
1Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. srijit@understanding.bio.
Journal of cheminformatics
|September 27, 2025
概括
我们开发了机器学习模型,使用分子结构和动物数据来预测人类的药理动力学 (PK) 参数. 我们免费提供的PKSmart工具和开源模型与药物发现的行业标准预测相匹配.
科学领域:
- 药理动力学和药物新陈代谢
- 计算化学和化学信息学
- 机器学习在药物发现中的作用
背景情况:
- 药物暴露对安全性和有效性至关重要,取决于药理动力学 (PK) 参数,如分布量 (VDss) 和清除量 (CL).
- 准确预测人类PK参数对于有效的早期药物发现至关重要.
- 现有的预测模型通常是专有的,限制了可访问性.
研究的目的:
- 开发和验证用于预测人类PK参数的机器学习模型 (VDss,CL,fu,t1⁄2,MRT).
- 为人类PK预测创建一个公开可访问的工具和开源模型.
- 将PK预测纳入早期药物发现工作流程.
主要方法:
- 一个使用分子结构,物理化学性质和预测动物PK数据的两阶段建模管道.
- 训练模型预测老鼠,狗和的PK参数,然后将这些预测用于人类PK建模.
- 采用随机森林模型,整合分子描述符,指纹和预测的动物PK值.
主要成果:
- 开发了人类PK参数的模型,在验证集中具有一致的性能.
- 获得了VDss的0.39和CL的0.46的外部R2值.
- 对于VDss,PK预测的准确性与专有行业模型相提并论.
- 发布了PKSmart网络应用程序和开源代码以实现广泛的可访问性.
结论:
- 本研究介绍了首个公开的PK预测模型套件,与行业标准性能相匹配.
- 开发的模型和工具通过提供可访问的人类PK预测来促进早期药物发现.
- 开源可用性和PKSmart应用程序促进了更广泛的融入药物开发过程.
相关概念视频
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