结合化学结构和概念知识,可以准确预测化合物-蛋白质相互作用
Wen Tao1, Xuan Lin2,3, Yuansheng Liu4,5
1College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.
BMC biology
|October 29, 2024
概括
BEACON是一个新的框架,将化学结构与生物医学知识图集集成,以改进化合物-蛋白相互作用 (CPI) 预测. 这种双重驱动的方法提高了准确性,特别是对于缺乏知识的化合物和蛋白质.
科学领域:
- 生物医学信息学 生物医学信息学
- 药物发现 药物发现 药物发现
- 计算生物学 计算生物学
背景情况:
- 准确的化合物-蛋白相互作用 (CPI) 预测对于药物发现至关重要.
- 当前的数据驱动方法往往忽略了生物医学知识图 (KG) 中的关键概念知识.
- 知识图提供了丰富的上下文信息,包括路径和疾病,以提高CPI预测的准确性.
研究的目的:
- 引入BEACON,这是一个双重驱动的框架,集成化学结构和KG概念知识,用于增强CPI预测.
- 在数据驱动模型中解决知识缺失的化合物和蛋白质的挑战.
- 通过利用多种数据源来提高消费者指数预测的准确性和范围.
主要方法:
- BEACON采用双重驱动的方法,将化学结构信息与KG概念知识相结合.
- 它通过最大限度地提高化学结构和概念知识之间的相互信息来学习一致的表示.
- 缺失的表示通过最小化它们的条件来预测.
主要成果:
- 在多个数据集上,BEACON实现了最先进的性能,超过了现有的方法.
- 在BIOSNAP上显示了5.1%的性能增长,在DrugBank数据集上显示了6.6%.
- 贝康是唯一能够预测缺乏先前知识的化合物和蛋白质的知识表示.
结论:
- 本书提出了将概念知识纳入CPI预测模型的可通用方法.
- 通过整合多种知识来源,BEACON有效地提高了基于数据的CPI预测的性能.
- 该框架提供了一个有前途的方向,通过更准确的相互作用预测来推动药物发现.
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