可解释的RNA-小分子结合亲和力预测基于多视图增强学习
Zeyu Wu1,2, Zhaohong Deng1,2, Qunzhuo Wu1,2
1School of Artificial Intelligence and Computer Science, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China.
Journal of chemical information and modeling
|June 3, 2025
概括
预测RNA-小分子结合亲和力对于药物发现至关重要. 一个新的深度学习模型,EMMPTNet,使用物理化学和拓特性准确预测结合亲和力,优于现有方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 由于RNA是潜在的药物标,因此需要选RNA与小分子相互作用的方法.
- 准确预测RNA-小分子结合亲和力是药物开发中的一个重大挑战.
研究的目的:
- 开发一种可解释的深度学习模型,用于预测RNA-小分子结合亲和力.
- 用物理化学和拓学属性准确预测结合亲和关系的挑战.
主要方法:
- 提出了一个可解释的多视图,多尺度深度学习网络 (EMMPTNet).
- EMMPTNet使用四个模块来有效地从多个数据视图中提取特征.
- 一个多层感知子从提取的多视图,多尺度特征中预测结合亲和力.
主要成果:
- EMMPTNet实现了0.058的平均绝对误差 (MAE) 和0.773.7的皮尔森相关系数 (PCC).
- 与当前最先进的方法相比,该模型表现出优越的性能.
- 对特征提取和重要性可视化的分析证实了模型的可解释性.
结论:
- EMMPTNet提供了一种有效和可解释的方法来预测RNA-小分子结合亲和力.
- 该模型的概括能力通过对新型化合物的验证得到证实.
- 这项工作通过改善关键分子相互作用的预测来推进计算药物发现.
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