一个多式对比式学习框架,用于预测P-糖蛋白基质和抑制剂
Yixue Zhang1,2, Jialu Wu1, Yu Kang1
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Journal of pharmaceutical analysis
|September 8, 2025
概括
这项研究介绍了MC-PGP,这是一种新型模型,通过整合各种分子特征来准确预测P-glycoprotein (P-gp) 抑制剂和基质. 这种先进的模型显著改善了药物发现和毒理学评估.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- P-糖蛋白 (P-gp) 显著影响药物ADMET的特性.
- 准确预测P-gp抑制剂/基质对于药物开发至关重要.
- 现有的模型缺乏全面的分子信息,限制了预测准确度.
研究的目的:
- 开发一种先进的计算模型,用于预测P-gp抑制剂和基质.
- 通过利用多模式分子数据来克服现有模型的局限性.
- 提高P-gp相关药物评估的准确性和可靠性.
主要方法:
- 编制了5943种P-gp抑制剂和4018种基质的大数据集.
- 开发了一种多式图形对比学习 (GCL) 模型 (MC-PGP).
- 集成的微笑,分子指纹和图形特征使用基于注意力的融合和GCL.
主要成果:
- 与12种最先进的方法相比,MC-PGP实现了更高的性能.
- 在外部数据集上,抑制剂的AUC-ROC改善率为9.82%,基质的AUC-ROC改善率为10.62%.
- 解释性分析揭示了参与P-gp相互作用的关键功能组.
结论:
- 在预测P-gp抑制剂和基质方面,MC-PGP提供了显著的进步.
- 该模型为药物设计提供了有价值的,化学直观的见解.
- 这种方法有助于优化候选药物和改善毒理学评估.
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