ToxiPep:通过融合上下文感知表示和原子级图表来预测类毒性
Jiahui Guan1, Peilin Xie2, Dian Meng1
1Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.
Computational and structural biotechnology journal
|June 18, 2025
概括
新的双模型框架ToxiPep通过结合序列和结构数据准确预测治疗性毒性. 该工具通过识别用于精密医学应用的更安全来增强药物开发.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 治疗药物具有很高的生物相容性和特异性.
- 类毒性是药物开发中的一个主要障碍.
- 强大的毒性预测方法至关重要.
研究的目的:
- 引入ToxiPep,一种用于类毒性预测的新型双模型框架.
- 整合基于序列的和原子级的结构特征,以提高预测准确度.
- 提供对毒性分子机制的见解.
主要方法:
- 开发了一个双模型框架,集成BiGRU和变压器进行序列分析.
- 利用分子图表上的多尺度CNN来提取结构特征.
- 采用交叉注意力机制来融合序列和结构信息.
主要成果:
- 与最先进的工具相比,Toxipep在内部和独立的测试集中表现出更高的性能.
- 确定了导致毒性的关键氨基酸和结构特征.
- 开发了一个可访问使用ToxiPep框架的Web服务器.
结论:
- 通过整合各种数据模式,ToxiPep有效地预测毒性.
- 该框架为毒性机制提供了有价值的见解.
- ToxiPep有可能加速开发更安全的治疗,用于精准医学.
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