在专家系统中使用卷积图注意力网络进行先进的药物目标相互作用预测
1Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamilnadu, India. mr1081@srmist.edu.in.
Molecular diversity
|August 2, 2025
概括
这项研究引入了用于预测药物向相互作用 (DTI) 的深度学习框架,显著提高了药物发现的准确性和效率. 这种新的方法提高了DTI预测系统的可靠性.
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.
- 药物发现 药物发现
背景情况:
- 预测药物向相互作用 (DTI) 对于加速药物发现和重新定位,减少时间和成本至关重要.
- 传统的DTI预测实验方法往往耗时且昂贵.
研究的目的:
- 为准确和高效的DTI预测开发一个先进的深度学习框架.
- 提高药物发现和重新利用管道的可靠性和效率.
主要方法:
- 开发了一个新的深度学习框架,即卷积多层极端对抗图注意力基础神经网络 (CMEAG-ANN).
- 一个基于快速相关的渐变天真贝叶斯和二进制模式选择 (FC-GNBBPS) 算法用于从DNA分子衍生数据中进行强大的特征提取.
- 图表注意力算法集成了药物和蛋白质的结构和进化方面,使用分子指纹和基于PSSM的注释.
主要成果:
- CMEAG-ANN模型实现了高性能指标:99.17%的准确性,99.11%的精度,98.83%的回忆,98.96%的F1得分,98.74%的特异性.
- 对基准数据集的实验评估表明CMEAG-ANN比基线模型的优越性.
- 基于生物学的特征选择增强了模型的预测能力.
结论:
- 拟议的CMEAG-ANN框架显著提高了DTI预测的准确性和效率.
- 这项研究证明了将基于图形的神经网络与用于DTI预测的高级特征选择集成的有效性.
- 这种方法为药物发现和重新利用提供了更可靠和更有效的方法.
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