在人类中使用渐变树增强中枢神经系统 (CNS) 小分子的半衰期预测
Hong Wang1, Pan Zhang1, Stephen J Barigye1
1Computational Science & Artificial Intelligence, Xenon Pharmaceuticals Inc, Burnaby, BC, Canada.
Future medicinal chemistry
|September 8, 2025
概括
一个机器学习模型准确地预测了开发早期中枢神经系统 (CNS) 药物的人类半衰期. 该工具有助于优先考虑候选药物,并利用精心策划的数据集来改进未来的预测.
科学领域:
- 药理动力学和药物开发
- 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:
- 准确预测中枢神经系统 (CNS) 药物的人类半衰期对于早期药物开发至关重要.
- 现有的方法往往缺乏有效的候选人优先级所需的精度.
- 综合体内和体内数据的综合数据集对于强大的预测建模至关重要.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于早期预测口服中枢神经系统药物的人类半衰期.
- 建立一个精心策划的数据集,包括关键的体外和体内数据,以支持未来的建模工作.
主要方法:
- 编制了76种中枢神经系统药物和候选药物的数据集,包括人类和老鼠的半衰期,血蛋白结合 (PPB) 和肝脏微小体清除 (LM) 数据.
- 渐变树增强 (GTB) 模型是使用ChemAxon的训练器引擎构建的.
- 用外部验证,基于相似性的数据分割和Y随机化来评估模型性能.
主要成果:
- 最好的ML模型在观察到的人类半衰期值的两倍之内实现了82.4%的预测 (R2=0.75,RMSE=0.25).
- 结合了体外,临床前体内数据和物理化学特性,显著提高了预测性能.
- 确定了驱动准确人类半衰期预测的关键特征.
结论:
- 开发的ML模型为中枢神经系统候选药物的早期预测和优先确定提供了实用实用.
- 精心策划的数据集是增强内部数据库和推进药物发现中的预测建模的宝贵资源.
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