多模式对比表示学习用于药物标结合亲缘关系预测
Linlin Zhang1, Chunping Ouyang1, Yongbin Liu1
1School of Computer, University of South China, Hengyang, China.
Methods (San Diego, Calif.)
|November 12, 2023
概括
准确的药物标结合亲和力 (DTA) 预测对于药物开发至关重要. 一个新的多式联络深度学习模型,FMDTA,有效地整合了各种药物和目标数据,优于现有的增强DTA预测方法.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算机化药物发现技术
- 机器学习在药理学中的应用
背景情况:
- 药物的疗效依赖于标相互作用,使得药物标结合亲和力 (DTA) 预测对于药物开发至关重要.
- 传统的DTA预测方法与大数据需求扎;深度学习显示出希望,但通常使用单个数据模式.
- 整合来自药物和目标的多式联络信息可以产生更全面,更准确的DTA预测.
研究的目的:
- 引入FMDTA,一种新的多式联络信息融合模型,用于药物标结合亲缘关系预测.
- 利用药物和目标信息的字符串和图形模式,以改善DTA预测.
- 通过使用对比学习和利用对齐信息来平衡多式联运数据来增强特征表示.
主要方法:
- 开发了FMDTA,这是一个深度学习模型,集成了药物和目标的字符串和图表表示.
- 采用对比学习来平衡跨不同模式的特征表示.
- 利用药物原子和目标残留物之间的对齐信息来捕获SMILES和目标序列中的位置模式.
主要成果:
- 与最先进的模型相比,FMDTA在两个基准数据集上表现出更好的表现.
- 该模型通过整合多式联运数据,有效地捕获了有价值的特征信息.
- 实验结果验证了FMDTA的可行性和强大的特征提取能力.
结论:
- 通过融合多式联络数据,FMDTA提供了一种强大的方法,可以通过融合多式联络数据,准确地预测药物标的结合亲和力.
- 该模型能够整合多种数据类型并捕获位置信息的能力提高了其预测能力.
- FMDTA代表了计算药物发现的重大进步,代码和数据公开可用.
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