基于任务相似性转移学习的口服生物可用性属性预测
Chen Zeng1, Chengcheng Xu1, Yingxu Liu1
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, 211198, China.
Molecular diversity
|September 10, 2025
概括
预测人类口服生物利用率 (HOB) 对药物开发至关重要. 一个新的转移学习框架 (TS-GTL) 有效地使用分子图和物理化学特性来改善HOB预测,特别是在数据稀缺的情况下.
科学领域:
- 药理动力学和药物发现
- 计算化学计算化学
- 人工智能在医学中的应用
背景情况:
- 准确预测人类口服生物利用率 (HOB) 对于优化候选药物和减少临床试验失败至关重要.
- 对HOB的实验性确定是资源密集的,而现有的AI模型面临数据依赖的挑战.
- 开发用于ADMET属性预测的高效和可解释的计算方法是研究的一个关键领域.
研究的目的:
- 开发一个高效和可解释的计算框架来预测人类口服生物利用率 (HOB),特别是在数据稀缺的环境中.
- 将物理化学特性与基于图形的深度学习相结合,以提高HOB预测的准确性.
- 引入一种新的转移学习方法,利用任务相似性来提高预测性能.
主要方法:
- 提出了一个以相似性为指导的转移学习框架,基于分子图 (TS-GTL) 的任务相似性为指导的转移学习.
- 开发了一个深度学习模型,PGnT (基于pKa图的知识驱动的变压器),结合了分子描述符并利用了GNN和变压器编码器.
- 引入了MoTSE来量化物理化学特性和HOB之间的任务相似性,对logD属性的预训练显示了最佳的转移学习性能.
主要成果:
- 与传统的机器学习算法和现有的深度学习预测工具相比,TS-GTL框架表现出卓越的性能.
- 转移学习,特别是在logD属性上进行预训练时,显著提高了HOB预测准确度.
- 该研究强调了任务相似性的重要性,以优化转移学习以预测药物属性.
结论:
- TS-GTL框架为ADMET属性预测提供了一种高效和可解释的替代实验和当前计算方法.
- 利用物理化学特性和基于图形的深度学习与任务相似性是改善HOB预测在数据有限的场景中的有希望的策略.
- 这种方法推进了AI在药物发现中的应用,使药物候选物的更好优化成为可能.
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