HyPepTox-Fuse:一种可解释的混合框架,用于准确的类毒性预测,将基于蛋白质语言模型的嵌入与常规描述符融合在一起
Duong Thanh Tran1, Nhat Truong Pham1, Nguyen Doan Hieu Nguyen1
1Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Journal of pharmaceutical analysis
|September 22, 2025
概括
预测毒性对于安全的药物开发至关重要. 一个新的计算框架,HyPepTox-Fuse,结合了蛋白质语言模型和传统描述器,以准确预测毒性,改进现有方法.
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:
- 类治疗药物具有显著的治疗潜力,但由于毒性而面临挑战.
- 准确预测毒性对于开发安全的基于的药物至关重要.
- 现有的毒性预测计算方法往往缺乏稳定性和通用性.
研究的目的:
- 开发一个新的计算框架,HyPepTox-Fuse,用于准确和强大的预测毒性.
- 提高酸毒性预测模型的概括性和性能.
主要方法:
- HyPepTox-Fuse集成了基于蛋白质语言模型 (PLM) 的嵌入与传统的描述器.
- 集成基于PLM的嵌入式是使用交叉模式的多头注意力机制和变压器架构生成的.
- 一个特征选择管道改进了传统的描述符,以提高预测准确度.
主要成果:
- 与最先进的方法相比,HyPepTox-Fuse在交叉验证和独立评估中都表现出更高的性能.
- 该框架在预测毒性方面表现出强度和通用性,通过案例研究验证.
- 开发的工具为毒性评估提供了一个可扩展和可靠的解决方案.
结论:
- HyPepTox-Fuse为预测毒性提供了一个有效和直观的平台.
- 开放的HyPepTox-Fuse服务器和源代码的可访问性促进了可重复性和进一步的研究.
- 该框架支持设计更安全的基于的治疗方法.
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