生物分子相互作用预测:人工智能时代
Haoping Wang1, Xiangjie Meng1, Yang Zhang1,2
1School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|July 17, 2025
概括
深度学习模型准确地预测生物分子相互作用,加速药物发现. 这种方法通过分析各种分子的序列,结构和功能数据来增强对分子生物学的理解.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 预测生物分子相互作用对于药物发现和分子生物学至关重要.
- 深度学习 (DL) 为复杂的生物数据提供了强大的模式识别.
- 现有的方法需要大量的时间和资源来选潜在的候选药物.
研究的目的:
- 为预测生物分子相互作用的深度学习算法提供全面的概述.
- 总结深度学习中用于生物分子相互作用预测的数据集和模型.
- 突出深度学习在加速药物发现和理解分子机制方面的潜力.
主要方法:
- 对应用到生物分子相互作用预测的深度学习算法的审查.
- 分析各种特征:序列数据,结构信息,功能注释.
- 蛋白质,核酸和小分子数据集和模型的摘要.
主要成果:
- 深度学习模型在预测生物分子相互作用方面表现出很高的准确性.
- 适用于各种分子标 (蛋白质,核酸,小分子).
- 确定关键的残留物和预测药物安全的非目标相互作用.
结论:
- 深度学习显著减少了用于药物发现的选化合物的时间和成本.
- DL提高了对生物分子相互作用机制的理解.
- 深度学习即将彻底改变药物发现和分子生物学研究.
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