MMPK:一种多式深度学习框架,用于预测人类口服药物动力学参数
Xiang Li1, Meiling Zhan1, Jiaojiao Fang1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Journal of medicinal chemistry
|July 31, 2025
概括
本研究介绍了MMPK,这是一种多式联络深度学习模型,用于预测人类口服药物动力学 (PK) 概况. MMPK准确地预测了体内的药物行为,节省了药物开发的时间和资源.
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
- 机器学习在药理学中的应用
背景情况:
- 准确预测体内药代动力学 (PK) 概况对于药物开发至关重要,影响安全性,疗效和剂量优化.
- 机器学习提供了一种有希望的方法来加速PK预测,减少药物发现所需的时间和资源.
- 现有的方法往往难以捕获与PK行为相关的复杂的多层次分子信息.
研究的目的:
- 开发一种新的深度学习框架,MMPK,用于预测人类口服药理动力学 (PK) 参数.
- 整合多样化的分子表示,包括分子图,亚结构图和SMILES序列,用于全面的特征提取.
- 通过多任务学习和数据归算技术来提高模型的效率和稳定性.
主要方法:
- 构建一个大型人类口服PK数据集,包括1200多种化合物和5000多种化合物剂量组合.
- 开发MMPK多式联网深度学习框架,集成图形和基于序列的分子表示.
- 实现多任务学习和数据归算,以优化从PK数据集中的学习.
主要成果:
- 与基线模型相比,MMPK在预测八个关键PK参数方面表现优越.
- 实现了2.895的平均几何平均折叠误差 (GMFE) 和0.599.59的根平均平方对数误差 (RMSLE).
- 该模型的有效性突显了整合多尺度分子信息用于PK预测的好处.
结论:
- 该MMPK框架提供了一个强大而准确的工具,用于预测人类口腔PK概况.
- 这种方法有很大的潜力通过提高PK评估的效率来简化药物开发.
- 为了促进进一步的研究和应用,MMPK模型及其基础数据集已公开提供.
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