基于化学结构的机器学习框架来预测小分子药物的药物动力学特征
Nikhil Pillai1, Alexandra Abos2, Donato Teutonico3
1Global DMPK Modeling & Simulation, Sanofi, Cambridge, Massachusetts, USA.
机器学习使用分子结构预测药物药理动力学 (PK) 概况,减少动物试验. 这种方法通过预测度-时间概况以及吸收,分布,新陈代谢和分泌 (ADME) 特性,有助于早期药物发现.
科学领域:
- 计算化学的计算化学
- 药理动力学 药理动力学
- 药物发现 药物发现 药物发现
背景情况:
- 准确预测药理动力学 (PK) 概况对于成功的药物发现至关重要.
- 目前的方法,如全尺度缩放和数学建模,依赖于广泛的动物实验,并且在早期机械学理解方面存在局限性.
研究的目的:
- 开发一种新的机器学习 (ML) 方法来直接预测小分子的度-时间概况.
- 为了减轻在早期药物发现阶段对动物进行广泛实验的需要.
主要方法:
- 开发了一个框架,首先从分子结构中预测吸收,分布,新陈代谢和消除 (ADME) 特性.
- 然后,这些预测的ADME属性被用作ML模型的输入来预测PK配置文件.
- 该方法利用结构驱动的分子特性作为ML模型的输入.
主要成果:
- 拟议的ML算法充分预测了测试化合物的PK概况.
- 对于Tanimoto分数大于0.5的化合物,预测和观察到的PK配置文件之间的平均绝对百分比误差小于150%.
结论:
- 开发的框架有效地使用ML和分子结构预测PK配置文件,减少对动物研究的依赖.
- 这种方法可以促进早期的PK预测,支持药物发现中的分子查和设计.
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