基于使用残余图形变压器网络的目标结构,改进对药物标结合点的识别
Shuang-Qing Lv1, Xin Zeng2, Guang-Peng Su2
1Faculty of Surveying and Information Engineering, West Yunnan University of Applied Sciences, Dali 671000, China.
Biomolecules
|February 26, 2025
概括
一个新的深度学习模型,RGTsite,通过整合目标结构和多式联络特征,准确地识别药物标结合点. 这加快了药物发现的速度,提高了对现有方法的预测准确度.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 准确识别药物标结合部位对于有效的药物查和设计至关重要.
- 目前的预测模型面临着由于多式联运信息融合不足和数据集不平衡的挑战.
研究的目的:
- 开发一个新的深度学习框架,RGTsite,用于增强药物标结合部位预测.
- 通过有效整合多样化的目标信息,克服现有方法的局限性.
主要方法:
- 使用一个残余图形转换器网络 (GTN),与残余1D卷积神经网络 (1D-CNN) 和ProtT5集成,用于特征提取.
- 结合了局部,全球序列特征和物理化学特性作为图的顶点特征.
- 嵌入边缘特征和应用GTN用于全面的特征提取和绑定站点分类.
主要成果:
- 在基准数据集上,RGTsite与最先进的方法相比,表现优越.
- 在F1得分 (F1) 和马修斯相关系数 (MCC) 等关键指标中获得更高的分数.
- 解释性分析证实了RGTsite在实际药物标结合位点识别中的有效性.
结论:
- RGTsite在预测药物标结合部位方面取得了重大进展.
- 该框架有效地解决了多式联运信息融合和数据集不平衡的挑战.
- 通过准确的结合部位识别,RGTsite有望加速药物开发管道.
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