KRN-DTI:向准确的药物向相互作用预测与科尔摩戈罗夫-阿诺德和残留网络
Zhen Li1, Juyuan Huang2, Xinxin Liu3
1Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong, 510006, China; School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China.
Methods (San Diego, Calif.)
|April 26, 2025
概括
本研究介绍了KRN-DTI,这是一种新的AI方法,用于使用可解释图形卷积网络 (GCN) 预测药物向相互作用 (DTI). KRN-DTI提高了准确性和可解释性,在药物发现中超越了现有的计算方法.
科学领域:
- 计算生物学是一种计算生物学.
- 人工智能在药物发现中的作用
- 网络药理学 网络药理学
背景情况:
- 准确预测药物向相互作用 (DTI) 对于有效的药物发现至关重要.
- 图形卷积网络 (GCN) 对DTI预测具有前景,但存在过度平滑和缺乏解释性问题.
- 现有的基于GCN的方法通常由于数据集依赖性而表现出有限的概括性.
研究的目的:
- 开发一种新的,可解释的基于GCN的方法 (KRN-DTI),用于准确的DTI预测.
- 解决过度平滑问题,提高DTI预测中的模型解释性.
- 提高计算药物发现工具的通用性和现实世界的适用性.
主要方法:
- 利用药物向异质网络进行DTI预测.
- 采用GCN技术,具有动态重量调整,以提高可解释性.
- 集成的剩余连接以减轻过度平滑的问题.
- 集成的科尔莫戈罗夫-阿诺德网络 (KAN) 和适应性重量调整的注意力机制.
主要成果:
- 在基准数据集上,KRN-DTI与几个先进的计算方法相比,表现优越.
- 该方法有效地利用已知的相互作用,并动态调整权重以改善预测.
- 案例研究验证了KRN-DTI在识别潜在的DTI方面的能力.
结论:
- KRN-DTI在使用人工智能进行可解释的DTI预测方面取得了重大进展.
- 该方法在加速药物发现管道的实际应用方面显示出强大的潜力.
- 公共可访问的代码和数据促进了该领域的进一步研究和开发.
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