预先训练的蛋白质语言模型与等价图神经网络的集成,用于酸毒性预测
Shihu Jiao1,2, Xiucai Ye3, Tetsuya Sakurai2
1Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
BMC biology
|July 28, 2025
概括
StrucToxNet通过整合序列和3D结构数据来改善毒性预测. 这种新的计算框架通过更准确的毒性查,提高了基于的治疗方法的安全性.
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
- 生物技术是生物技术.
背景情况:
- 类疗法提供了多功能性和特异性,但由于固有的毒性而面临挑战.
- 目前用于酸毒性查的计算方法往往忽略了关键的3D结构信息,限制了预测准确度.
- 准确预测毒性对于推动基于的药物开发至关重要.
研究的目的:
- 开发一个新的计算框架,StrucToxNet,用于增强毒性预测.
- 整合顺序和3D结构信息,以提高毒性查的准确性.
- 为识别更安全的疗法提供可靠的工具.
主要方法:
- 开发了StrucToxNet,一个框架,将预训练的蛋白质语言模型 (ProtT5) 与等价图形神经网络相结合.
- 利用ESMFold预测的3D结构数据与序列嵌入一起.
- 在使用各种预测指标的独立数据集上评估性能.
主要成果:
- StrucToxNet有效地捕捉了的顺序和空间特征.
- 与现有的基于序列的模型相比,该框架显示出更高的性能.
- 在预测毒性方面实现了更高的平衡精度和整体性能.
结论:
- StrucToxNet被证明是用于计算毒性查的强大和可通用的工具.
- 这些发现有助于开发更安全的基于的治疗方法.
- 这种方法提高了药物开发管道中识别有毒的可靠性.
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