DMPE:一个双分支分子性质封装框架,使用Kolmogorov-Arnold网络
Lihui Duo1, Bencan Tang1, Jonathan D Hirst2
1Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, China.
Journal of computational chemistry
|November 14, 2025
概括
我们开发了一种双分支分子性质封装 (DMPE) 框架,用于药物发现. 这种先进的模型改善了分子性质的预测,并确定了肝细胞癌的潜在抑制剂.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 生物信息学是一种生物信息学.
背景情况:
- 准确的分子表示对于预测药物特性至关重要.
- 现有的方法可能无法完全捕捉复杂的分子结构和关系.
- 加快药物发现需要高效可靠的预测模型.
研究的目的:
- 引入双分支分子性质封装 (DMPE) 框架.
- 增强分子表示学习,用于属性预测.
- 为了识别肝细胞癌的潜在的造血原体基因酶1 (HPK1) 抑制剂.
主要方法:
- 使用精制的交互式图表注意力框架 (RIGAF) 来捕捉分子内和分子间的特征.
- 使用基于Kolmogorov-Arnold网络 (KAN) 的嵌入和融合 (KAEF) 模块集成图形特征和分子指纹.
- 实施蒙特卡洛脱落的不确定性估计和整体策略.
主要成果:
- 在BBBP (0.927) 和SIDER (0.691) 基准上获得了优异的ROC-AUC得分.
- 在14个乳腺癌细胞系中的9个细胞系中表现出有希望的准确性.
- 通过废弃研究验证了关键的建筑组件.
结论:
- DMPE框架在分子性质预测方面提供了竞争力的表现.
- 该KAEF模块增强了可解释性和通用性.
- DMPE显示出识别新型候选药物的潜力,其应用在HPK1抑制剂中就是一个例子.
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