通过多种描述器类推进口服药物的深度学习和机器学习模型的开发:专注于药物动力学参数 (Vdss和PPB)
Rakesh Bantu1, Samiron Phukan2, Simon Haydar1
1Integrated Drug Discovery, Aragen Lifesciences Ltd, Hyderabad, 500076, India.
Molecular diversity
|June 11, 2025
概括
这项研究引入了预测性深度学习和机器学习模型,以准确预测药物动力学参数,如分布量和血蛋白结合,为分子描述器角色提供了新的见解.
科学领域:
- 计算化学和药理学计算化学和药理学
- 药物的发现和开发.
- 生物信息学是一种生物信息学.
背景情况:
- 药物动力学 (PK) 参数,包括分布量 (Vdss) 和血蛋白结合 (PPB),对于药物开发至关重要.
- 在药物发现过程的早期预测这些PK参数可以显著提高效率并降低成本.
- 现有的PK参数预测方法往往缺乏准确性或需要广泛的实验数据.
研究的目的:
- 开发和验证用于VDSS和PPB的预测深度学习 (DL) 和机器学习 (ML) 模型.
- 调查分子描述符在确定这些PK参数中的作用.
- 使用先进的特征工程技术,识别驱动VDSS和PPB的显著分子特征.
主要方法:
- 使用了FDA批准的药物的数据集,报告了口服PK参数.
- 计算了各种类型的分子描述符.
- 应用Boruta算法用于特征工程,以选择重要的描述符.
- 训练和评估多个ML算法,包括梯度提升和堆叠分类器,用于VDSS和PPB预测.
- 基于VDSS和PPB之间共享的描述符开发模型.
主要成果:
- 实现了VDSS (高达80%的梯度提升) 和PPB (高达73%的随机森林) 的高预测准确度.
- 博鲁塔算法通过识别关键分子特征,显著提高了模型的准确性.
- 确定了特定的量子化学 (MLFER) 和拓描述符 (piPC) 作为Vdss和PPB的共同驱动因素.
结论:
- 开发的DL和ML模型为VDSS和PPB提供了准确的预测.
- 使用Boruta算法的特征工程有效地改善了PK参数预测.
- 确定了影响VDSS和PPB的关键分子描述因子,为药物设计提供了宝贵的见解.
相关概念视频
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