调整:通过无监督的SMILES调整,推动无模板回复合成预测的极限
Kaipeng Zeng1, Bo Yang2, Xin Zhao1
1MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China.
Journal of cheminformatics
|July 15, 2024
概括
UAlign是一种新的无模板人工智能方法,通过使用图形神经网络和变压器来更好地理解分子结构,改善了回复合成预测. 这种方法的性能优于现有的方法,为有机化学和制药开发提供了一个强大的新工具.
科学领域:
- 有机化学 有机化学
- 计算化学的计算化学
- 科学中的人工智能.
背景情况:
- 复合成规划是化学工业,特别是制药行业的一个重大挑战.
- 单步逆合成预测是一个关键的过程,它受益于最近的AI进步.
- 现有的深度学习方法往往对化学知识有不同的依赖.
研究的目的:
- 介绍UAlign,一个新的无模板图形到序列管道用于回归合成预测.
- 在基于变压器的方法中克服分子表示学习和化学信息利用的局限性.
- 提高从产品中预测化学反应物的效率和准确性.
主要方法:
- 开发了一个无模板的图形到序列管道,将图形神经网络和变压器结合起来.
- 实施了SMILES对齐技术,以重复使用不变的分子结构用于反应剂生成.
- 利用无监督的学习机制,与反应物 SMILES 代币进行产品原子对应.
主要成果:
- UAlign有效地利用了分子固有的图形结构.
- 该方法显著优于最先进的无模板和半模板方法.
- 与最强的基线相比,获得了高达5% (前五名) 和5.4% (前十名) 的精度改进.
结论:
- UAlign在没有模板的回归合成预测方面表现出卓越的表现.
- 该方法的有效性与基于模板的强大方法相美,甚至超过了它们.
- 在有机化学中,UAlign为人工智能驱动的逆合成规划提供了有前途的进步.
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