CompBind:复杂指导预训基于结构的无蛋白质-连接物亲和力预测
Duoyun Yi1,2, Yanpeng Zhao3, Huiyan Xu1,2
1Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China.
Journal of chemical information and modeling
|January 21, 2026
概括
CompBind仅使用序列来预测蛋白质-连接体结合亲和力,从而绕过了对3D结构的需求. 这种新的深度学习框架通过提高准确性来加速药物发现,特别是在具有挑战性的场景中.
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
- 机器学习是机器学习.
背景情况:
- 准确的蛋白质 - 配体结合亲和力预测对于药物发现至关重要.
- 目前基于结构的深度学习方法受到实验复杂结构的稀缺性和成本的限制.
研究的目的:
- 开发一种新的框架,CompBind,用于不需要3D结构输入的绑定亲和力预测.
- 为了使准确的预测只使用蛋白质和连接体序列.
主要方法:
- CompBind将双向交叉注意力与双重目标的预训练策略相结合.
- 对比式学习强制执行单体和复合体之间的特征一致性.
- 生成式学习为基于序列的推理重建交互特征.
主要成果:
- CompBind在亲和力预测中优于非复杂的方法,并与复杂的方法竞争.
- 该模型在冷启动和稀疏标签条件下表现出强的性能.
- 在针对GPX4的药物重定向案例研究中,CompBind成功识别了已知的抑制剂.
结论:
- CompBind为结合亲和力预测提供了一个可扩展和通用化的解决方案,将准确性与实验结构可用性脱.
- 该框架加速了药物发现管道,并通过其注意力机制提高了模型的解释性.
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