药物成形器DTI:用于药物向相互作用预测的药物图形器
Jiayue Hu1, Wang Yu1, Chao Pang1
1School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
Computers in biology and medicine
|May 27, 2023
概括
这项研究介绍了DrugormerDTI,一种用于药物向相互作用预测的新型神经网络. 它增强了特征表示,在药物发现中表现优于现有的方法.
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) 的预测对于加速药物发现至关重要.
- 当前的计算方法经常与不充分的特征表示作斗争,限制了预测准确性.
研究的目的:
- 提出一种新的神经网络架构,DrugormerDTI,以改善用于DTI预测的特征表示.
- 解决捕获复杂分子信息的现有方法的局限性.
主要方法:
- 开发了DrugormerDTI,这是一个集成图形转换器的神经网络,用于分子图形分析 (序列和拓信息).
- 使用Resudual2vec来学习蛋白质残留物之间的关系.
- 进行了废弃实验以验证组件贡献,并将注意力映射与分子对接进行了比较.
主要成果:
- 与基线方法相比,DTI在四个基准数据集中表现出卓越的性能.
- 废弃性研究证实了DrugormerDTI中的每个成分的重要性.
- 该模型展示了有效的特征提取和表达能力,通过注意层映射和分子对接来验证.
结论:
- 拟议的DrugormerDTI模型显著提高了药物向相互作用预测的准确性.
- 集成图形变压器和特定残留设计是增强DTI预测模型的有效策略.
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