优化多任务学习与进化相关性指标,以提高基于QSAR的自然产品活动预测
Donny Ramadhan1,2,3, Reiko Watanabe2, Kenji Mizuguchi1,2
1Graduate School of Science, The University of Osaka, Toyonaka, Osaka 560-0043, Japan.
ACS omega
|September 29, 2025
概括
多任务学习 (MTL) 通过结合蛋白质的进化相关性,提高了对天然产品生物活性的预测. 这种方法增强了药物发现,特别是有限的数据.
科学领域:
- 生物化学 生物化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 自然产品具有复杂的结构,对药物向相互作用至关重要.
- 有限的生物活性数据阻碍了对天然产品的定量结构-活性关系 (QSAR) 预测.
- 多任务学习 (MTL) 是一个有前途的策略,可以克服QSAR的数据稀缺.
研究的目的:
- 通过整合蛋白质进化相关性来优化MTL,以提高天然产品生物活性预测.
- 为了确定MTL在稀疏数据集中最有效的条件.
- 改善天然产品的药物发现工作.
主要方法:
- 一个精心策划的自然产品和它们对酶的生物活性数据集是从ChEMBL构建的.
- 单任务学习 (STL) 被用作基线.
- 应用了基于特征的MTL (FBMTL) 和基于实例的MTL (IBMTL),结合了进化相关性.
- 对不同蛋白质组的性能进行了评估.
主要成果:
- 基于实例的MTL (IBMTL) 在预测天然产品生物活性方面表现优于STL和FBMTL.
- 进化相关性显著改善了预测性能,特别是对于酶和细胞染色体P450蛋白质组.
- 在酶组中,在目标母体水平上观察到最佳性能,这表明相关性和数据大小之间的平衡.
结论:
- 在MTL框架内利用蛋白质的进化相关性可以提高对自然产品的QSAR预测,即使数据有限.
- MTL,特别是IBMTL,显示了促进基于天然产品的药物发现的巨大潜力.
- 这项研究强调了在预测建模中考虑蛋白质层次和进化背景的重要性.
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