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BiToxNet:一个深度学习框架,集成多模式功能,用于准确识别神经毒性和蛋白质

Feng Wang^1,2, Peilin Xie^3,4, Xingqiao Lin²

¹School of Informatics, Xiamen University, 361005, Xiamen, China.

|February 26, 2026

概括

BiToxNet是一个新的深度学习框架,通过整合进化和生物化学数据,准确地预测和蛋白质的神经毒性. 这种计算工具增强了蛋白质治疗药物的安全性评估,并有助于药物开发.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
机器学习在药物发现中的作用

背景情况:

准确预测和蛋白质的神经毒性对于药物安全和开发至关重要.
实验方法对于大规模查来说是昂贵且耗时的.
现有的计算方法由于特征工程和融合策略的有限性而缺乏准确性.

研究的目的:

开发一个强大的深度学习框架来预测神经毒性.
提高计算神经毒性预测的准确性和通用性.
为神经毒素查和蛋白质药物安全提供一个有价值的工具.

主要方法:

开发了BiToxNet,这是一个深度学习框架,集成了来自蛋白质大语言模型的进化嵌入和十个手工制作的生物化学描述器.
采用双线性注意网络 (BAN) 来建模跨模式相互作用和残留水平依赖.
对蛋白质,和不同序列长度的组合数据集进行BiToxNet评估.

主要成果:

生物毒网实现了高精度:92.3% (蛋白质),96.0% () 和92.7% (组合).
该框架始终优于现有的最先进的方法.
废除研究和可视化分析证实了集成特征和BAN的重要性,证明了强大的泛化能力.

结论:

BiToxNet提供了一个强大的和可泛化的计算框架,用于识别神经毒性和蛋白质.
通过二线性注意力整合进化和生物化学信息提供了一个新的建模策略.
生物毒网作为神经毒素查和蛋白质治疗安全性评估的宝贵工具.

关键词:

双线性注意力网络是一个双线性注意力网络.深度学习是一种深度学习.药物发现药物发现神经毒素是一种神经毒素.序列分析是指进行序列分析.

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