RetroRanker:利用反应变化,通过重新排名来改善回复合成预测
Junren Li1, Lei Fang2, Jian-Guang Lou3
1College of Chemistry and Molecular Engineering, Peking University, No. 5 Yiheyuan Road, Beijing, 100871, China.
Journal of cheminformatics
|June 8, 2023
概括
RetroRanker是一个新的图形神经网络模型,它解决了数据驱动的回归合成预测中的频率偏差. 它重新排名预测,以提高有机化学合成计划的准确性.
科学领域:
- 有机化学 有机化学
- 计算化学的计算化学
- 化学合成 化学合成
背景情况:
- 数据驱动的方法正在推进反合成,但遭受频率偏差,导致次优预测.
- 频率偏差导致化学上不合理的反应物因训练数据分布而被排名高.
- 现有的基于模板的方法可能会在低等级,低信心预测中错过有效的反应物.
研究的目的:
- 介绍RetroRanker,一种基于神经网络的新型图形排名模型.
- 通过重新排名来减轻现有的逆合成预测模型中的频率偏差.
- 为了提高回复合成预测的准确性和可靠性.
主要方法:
- 开发了RetroRanker,这是一个图形神经网络模型,用于重新排名回归合成预测.
- RetroRanker评估预测反应物的潜在反应变化,以评估化学合理性.
- 该模型重新排名预测,降低化学不合理结果的排名.
主要成果:
- RetroRanker在大多数最先进的回复合成模型上展示了性能改进.
- 在公共回复合成基准上重新排名的结果显示了显著的改进.
- 初步研究表明,RetroRanker还可以提高多步复合性能.
结论:
- RetroRanker有效地减少了回归合成预测中的频率偏差.
- 该模型提高了化学合成规划工具的准确性.
- RetroRanker对单步和多步回复合成应用都非常有希望.
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