提高深度化学反应预测,通过先进的性和碎片表示来提高化学反应预测
Fabrizio Mastrolorito1,2, Fulvio Ciriaco3, Orazio Nicolotti2
1Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS) & Eindhoven AI Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands. f.grisoni@tue.nl.
概括
一种新的分子表示法,fragSMILES,在有机反应预测方面表现出色. 它在预测化学合成方面优于现有的方法,并准确地捕捉立体化学,推进计算机辅助规划.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在化学中的应用
背景情况:
- 准确的有机反应预测对于合成规划至关重要.
- 现有的分子表征很难有效地编码复杂的化学信息,包括立体化学.
研究的目的:
- 为基于深度学习的有机反应预测引入和评估fragSMILES分子表示.
- 为了比较fragSMILES与已经确立的符号,如SMILES,SELFIES,SAFE和t-SMILES.
主要方法:
- FragSMILES表示的发展,编码分子子结构和奇拉性.
- 系统性绩效评估前和后综合预测任务.
- 与简化分子输入线输入系统 (SMILES),自我引用嵌入字符串 (SELFIES),基于序列附件的片段嵌入 (SAFE) 和基于树的SMILES (t-SMILES) 的比较.
主要成果:
- 在前向和后向合成预测任务中,fragSMILES实现了最高的性能.
- 与其他符号相比,fragSMILES表现出对立体化学反应信息的更好识别.
- 该表示有效地捕捉了立体化学复杂性,这是合成规划中的一个重大挑战.
结论:
- 像fragSMILES这样的奇拉性意识和碎片级分子表示可以显著推进计算机辅助合成规划.
- fragSMILES为化学中的深度学习应用提供了一个紧而富有表现力的分子表示.
- 这项工作强调了针对复杂化学预测任务量身定制的分子表示的重要性.
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