药物向相互作用/亲和力预测:深度学习模型和进展审查
Ali Vefghi1, Zahed Rahmati1, Mohammad Akbari1
1Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
Computers in biology and medicine
|July 3, 2025
概括
深度学习模型通过改善药物向相互作用预测来加速药物发现. 这篇评论分析了180种机器学习方法,以提高开发救命药物的效率.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物发现是一个漫长的,昂贵的,安全性关心的过程.
- 传统方法在复杂的药物目标关系中扎.
- 准确预测药物向相互作用对于更快的药物开发至关重要.
研究的目的:
- 为研究人员提供预测药物向相互作用和亲和力的先进方法的概述.
- 突出这一领域有前途的研究途径和模型.
- 加速开发更有效的药物.
主要方法:
- 从2016-2025年对180种药物向相互作用/亲和力预测方法的分析.
- 专注于机器学习,特别是深度学习和图形神经网络.
- 讨论模型的新性,架构和输入表示.
主要成果:
- 深度学习模型显示出克服传统方法局限性的巨大潜力.
- 确定了各种框架和方法,以准确有效地预测相互作用.
- 综合分析涵盖了十年的计算药物发现研究.
结论:
- 深度学习和图形神经网络是推动药物向相互作用预测的关键.
- 在这些领域进行进一步的研究可以显著加快新疗法的交付速度.
- 本综述是为寻求提高药物发现效率的研究人员提供指南.
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