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KANPM-DTA:通过Kolmogorov-Arnold网络和预训练模型改进药物向 afinity 的预测

M D Youshuf Khan Rakib¹, Muhammad Habibulla Alamin¹, Jiamu Li²

¹School of Computer Science and Engineering, Central South University, Changsha, 410083 Hunan, China.

Briefings in bioinformatics

|March 12, 2026

概括

本研究介绍了KANPM-DTA,这是一种用于准确预测药物向亲和力 (DTA) 的深度学习框架. 该模型增强了概括性和解释性,显示了与药物发现现有方法相比的显著改进.

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.
机器学习在药物发现中的作用

背景情况:

准确的药物向亲和力 (DTA) 预测对于有效的药物发现和重新定位至关重要.
现有的DTA模型在泛化,解释性和整合多样化的生物数据方面面临挑战.
需要先进的计算框架来提高DTA预测可靠性.

研究的目的:

开发和验证KANPM-DTA,这是一种用于增强DTA预测的新型深度学习框架.
提高DTA预测模型的概括性和可解释性.
为了有效地整合异质的生物特征,以便更准确地预测亲缘关系.

主要方法:

开发了KANPM-DTA,这是一个深度学习框架,利用ESM引导的蛋白质图形构建策略.
采用封闭的融合机制来整合药物-蛋白质图的特征和对交叉模式依赖的线性关注.
利用Kolmogorov-Arnold网络进行非线性近似,并提高亲和预测中的解释性.

主要成果:

在基准数据集 (戴维斯,KIBA,梅茨,BindingDB) 上,KANPM-DTA显著超过了最先进的方法.
取得了显著的改善,包括MSE减少 (高达6.42%),CI增加 (高达0.80%),以及$r_{m}^{2}$增长 (高达1.85%).
一个案例研究证明了KANPM-DTA在预测以前未见的药物标对的DTA方面的有效性.

结论:

KANPM-DTA代表了DTA预测的重大进步,提供了更好的准确性和可解释性.
该框架显示了现实世界的应用在加速药物发现管道中的强大潜力.
建议进行进一步的湿实验室验证,以确认预测结果的实际适用性.

关键词:

科尔摩戈罗夫阿诺尔德的网络.注意力机制注意力机制药物目标亲和力图形神经网络的神经网络预先训练的模型.

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