毒性预测的最新进展:深度图形学习的应用.
Yuwei Miao1, Hehuan Ma1, Junzhou Huang1
1Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas 76019, United States.
Chemical research in toxicology
|August 10, 2023
概括
深度图形学习模型提供高效准确的药物毒性预测,加速药物开发. 这些先进的方法提供了更好的洞察力,改善了模型解释和概括,以实现更安全的药物发现.
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:
- 药物开发是昂贵和耗时的,需要准确的毒性预测,以确保安全性和有效性.
- 深度图形学习 (DGL) 为预测药物毒性提供了计算能力和成本效益.
- 现有的方法需要全面了解DGL组件和毒理学中的应用.
研究的目的:
- 将基础知识与用于药物毒性预测的先进深度图形学习方法相结合.
- 提供DGL组件的全面概述,包括分子描述符,表示,指标,验证和数据集.
- 审查代表性的DGL研究和方法,重点关注GNN架构和图形预训练模型.
主要方法:
- 总结用于毒性预测的DGL模型的基本组成部分.
- 分析各种基于图形的分子表示.
- 从GNN架构和图形预训练模型的角度介绍代表性研究.
主要成果:
- 与其他方法相比,深度图形模型显示出更高的准确性和效率.
- DGL模型提供了更直观的见解,增强了模型的解释和概括.
- 图表预训练模型显示出从大型未标记的分子数据中提取特征的前景,以改善毒性预测.
结论:
- 深度图形学习是推动药物毒性预测的强大而有效的工具.
- 图表预训练模型正在成为提高下游毒性预测任务的关键推动者.
- 本综述为进入DGL领域的研究人员提供了指南,用于药物毒性预测.
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