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一个域名适应性可解释的基础结构意识图表注意力网络用于药物相互作用预测
Qi Zhang1, Yuxiao Wei2, Liwei Liu3
1College of Science, Dalian Jiaotong University, Dalian, 116028, China.
Interdisciplinary sciences, computational life sciences
|January 8, 2025
概括
预测药物相互作用对药物安全至关重要. SAGAN是一个新的图表注意力网络,准确地识别了DDI预测的关键子结构,并增强了跨领域的概括性.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 准确的药物相互作用 (DDI) 预测对于临床疗效和药物安全至关重要.
- 对于DDI预测的现有计算方法往往忽略了关键的亚结构特征,并与跨域泛化作斗争.
研究的目的:
- 引入SAGAN,一个域适应性,可解释性,基础结构意识的图表注意力网络,用于增强DDI预测.
- 通过结合亚结构洞察力和改进概括能力来解决当前DDI预测模型的局限性.
主要方法:
- 开发了一种使用注意力机制和无监督集群来识别关键交互区域的新型子结构细分方法.
- 应用了条件域对抗网络,通过优化源域和对抗损失来实现跨域概括.
- 在域内和跨域情景中对四个现实数据集进行了SAGAN的评估.
主要成果:
- 与最先进的DDI预测模型相比,SAGAN在所有评估的数据集和场景中表现出卓越的性能.
- 该模型成功地提取了具有药理意义的亚结构,有助于识别新的相互作用部位.
- 可视化结果证实了该模型能够精确地确定涉及药物相互作用的关键区域.
结论:
- 通过整合子结构意识和域调整,SAGAN在DDI预测方面取得了重大进展.
- 该模型的解释性和概括能力为药物开发人员在选和优化方面提供了宝贵的工具.
- 开发的基础结构意识的方法可以帮助发现新的局部相互作用点和提炼药物结构.
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