MGF-DTA:一种多颗粒度融合模型,用于药物标结合 afinity 预测
Zheng Ni1, Bo Wei1, Yuni Zeng1
1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
International journal of molecular sciences
|January 28, 2026
概括
一个新的多颗粒度融合模型 (MGF-DTA) 通过整合多种药物特征和多个规模的蛋白质信息来增强药物向亲和力预测. 这种方法克服了当前药物发现方法的局限性.
科学领域:
- 计算生物学 计算生物学
- 药物发现 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:
- 药物标亲和力 (DTA) 预测对于有效的药物发现至关重要.
- 现有的方法在有限的药物多模式数据,蛋白质序列长度约束和不足的特征提取方面扎.
研究的目的:
- 开发一种先进的模型,MGF-DTA,用于改进药物标结合亲和力预测.
- 为了解决多模式药物信息和蛋白质序列中多尺度特征捕获的局限性.
主要方法:
- 利用ChemBERTa-2从SMILES字符串中提取深度语义特征,并通过封闭融合与分子指纹集成.
- 使用残留聚变将全球蛋白质嵌入 (ESM-2) 与本地k-mer和PCA特征相结合.
- 从药物和蛋白质序列中实现了对多颗粒度特征提取的层次关注机制.
主要成果:
- 与主流方法相比,MGF-DTA在戴维斯,KIBA和BindingDB数据集上表现优越.
- 废弃性研究证实了单个模型组件的显著贡献.
- 案例研究强调了该模型强大的概括能力.
结论:
- MGF-DTA模型通过利用多模式药物信息和多粒度蛋白质特征,有效地提高了药物向 afinity 预测.
- 这种方法为计算药物发现和开发提供了有希望的进步.
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