深度学习辅助的频谱结构相关性:最新情况和前景
Xin-Yu Lu1,2, Hao-Ping Wu3, Hao Ma1,2
1State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.
Analytical chemistry
|April 25, 2024
概括
深度学习正在通过实现精确的光谱预测和分子生成来彻底改变光谱结构相关性. 这种方法克服了传统方法的局限性,加速了化学分析和发现.
科学领域:
- 计算化学的计算化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 频谱结构相关性在光谱分析中至关重要,最近的进展是由高通量检测和扩大的化学空间推动的.
- 传统的化学测量与大量生成的数据作斗争,需要先进的分析方法.
研究的目的:
- 审查深度学习在建立频谱结构相关性的应用.
- 探索深度学习在光谱预测 (前进) 和逆光谱结构相关性 (图书馆匹配,de novo生成) 中的作用.
主要方法:
- 介绍分子/光谱表示和深度学习的基础知识.
- 在过去五年中,对频谱结构相关性应用的深度学习方法的总结.
主要成果:
- 深度学习显著提高了光谱预测的准确性.
- 深度学习促进了逆光谱结构相关性,有助于图书馆匹配和新型分子设计.
结论:
- 深度学习提供了一种强大的方法来克服频谱结构相关性传统化学测量的局限性.
- 深度学习的持续发展有望为频谱结构相关性解锁最终解决方案,推动跨科学学科的进步.
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