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通过图形神经网络对药物向相互作用和亲和力预测方法的调查
Yue Zhang1, Yuqing Hu1, Na Han1
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
Computers in biology and medicine
|June 17, 2023
概括
本研究探讨图形神经网络 (GNN),用于预测药物向相互作用 (DTI) 和药物向亲和力 (DTA). GNNs提供了一种强大的计算方法来加速药物发现,克服传统实验方法的局限性.
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) 和药物向 afinity (DTA) 的预测对于药物发现至关重要.
- 对于DTI/DTA的实验方法是昂贵且耗时的.
- 计算方法加速药物发现,但传统的特征表示 (结构,序列) 缺乏丰富性.
研究的目的:
- 用图形神经网络 (GNN) 提供DTI和DTA预测的概述.
- 突出基于分子图表的表征在捕获丰富的特征信息方面的优势.
- 为了证明GNN对DTI和DTA预测的有效性.
主要方法:
- 利用药物的分子图表表示.
- 采用主要序列和对目标蛋白的图形表示.
- 应用图形神经网络 (GNN) 进行预测任务.
- 在基本数据集上进行实验.
主要成果:
- 图形神经网络显示出药物向相互作用预测的前景.
- GNNs有效地预测药物标亲和力.
- 基于分子图的表示方法提供了比基于结构或基于序列的方法更丰富的特征.
- 实验验证支持在各种数据集上使用GNN.
结论:
- 图形神经网络是用于DTI和DTA预测的可行和有效的计算工具.
- 分子图表表示提高了药物发现过程的准确性和效率.
- 在药理学中,GNN为传统的实验和计算方法提供了强大的替代方案.
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