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MIF-DTI:一种用于药物向相互作用预测的多式联络信息融合方法
Jiehong Shan1,2, Jinchen Sun1,2, Haoran Zheng1,2
1School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China.
Briefings in bioinformatics
|September 18, 2025
概括
这项研究介绍了MIF-DTI和MIF-DTI-B,这是用于药物向相互作用 (DTI) 预测的新方法. 通过融合多式联络信息,这些模型显著提高了药物发现和重新利用的预测准确性.
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.
- 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 的预测对于开发新疗法和重新使用现有药物至关重要.
- 目前的DTI预测方法往往受到单源数据编码和不充分的多式联络信息融合的限制.
研究的目的:
- 开发一种先进的DTI预测方法,有效地整合多式联运信息.
- 提高DTI预测模型的准确性和稳定性.
主要方法:
- 提出了一种多模式信息融合 (MIF-DTI) 方法,利用序列和图形编码模块从药物和目标中提取1D序列特征和2D拓结构.
- 通过通过交叉验证将多个MIF-DTI模型结合起来,以提高预测性能,开发了一个整体版本 (MIF-DTI-B).
- 采用解码模块来有效地融合各种数据模式.
主要成果:
- 与现有最先进的方法相比,MIF-DTI和MIF-DTI-B在三个公共DTI数据集上都表现出优异的性能.
- 多式联运信息的全面整合是提高预测准确性的关键.
结论:
- 拟议的MIF-DTI和MIF-DTI-B模型通过有效利用多式联网数据,在DTI预测方面取得了重大进展.
- 这些方法为加速药物发现和重新定位努力提供了有希望的方法.
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