用预训练的图形编码器和语言模型预测激酶抑制剂结合亲和力

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Xudong Guo¹, Zixu Ran¹, Fuyi Li^1,2

¹College of Information Engineering, Northwest A&F University, Yangling, 712100, China.

Briefings in bioinformatics

|July 15, 2025

概括

预测抑制剂-激酶结合亲和力对于药物发现至关重要. 新的Kinhibit框架使用先进的AI显著提高了预测准确性,为癌症治疗和药物查提供了更好的工具.

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
生物信息学是一种生物信息学.

背景情况:

准确预测抑制剂-激酶结合亲和力对于药物发现和癌症治疗至关重要.
目前的预测方法在数据表示,特征提取和捕获复杂的激酶抑制剂相互作用方面存在困难.
先进的人工智能 (AI) 和深度学习技术显示出希望,但需要进一步开发复杂的分子相互作用.

研究的目的:

开发一种新的框架,Kinhibit,用于增强抑制剂-激酶结合亲和力预测.
解决现有方法的局限性,包括数据表达和特征提取不足.
提高药物查和生物科学中的计算工具的准确性和有效性.

主要方法:

Kinhibit集成了自我监督的图形对比学习,以有效地提取特征.
多视图分子图表表示能够捕捉到各种分子特征.
一个结构信息化的蛋白质语言模型 (ESM-S) 和特征融合优化了抑制剂-激酶相互作用分析.

主要成果:

在预测三种关键的基因激活蛋白激酶 (MAPK) 路径激酶 (RAF,MEK,ERK) 的抑制剂结合时,Kinhibit获得了92.6%的准确性.
该框架在全面的MAPK-All数据集上表现出卓越的性能,准确度为92.9%.
实验结果验证了拟议的特征提取和融合策略的有效性.

结论:

基尼希特在抑制剂-激酶结合亲和力预测方面提供了显著的进步.
该框架为加速药物查提供了一个有希望和有效的计算工具.
这种方法增强了我们理解和向癌症等疾病中酶通路的能力.

关键词:

结合性亲和力是一种结合性亲和力.相反的学习学习学习.图表神经网络的神经网络抑制剂抑制剂的使用激酶激酶的作用是什么

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