利用语言模型的多任务处理来预测C-H玻里लेशन选择性
Ruslan Kotlyarov1, Konstantinos Papachristos2, Geoffrey P F Wood2
1Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Journal of chemical information and modeling
|May 6, 2024
概括
在C-H玻里化反应中预测区域选择性是具有挑战性的. 一个微调的T5Chem语言模型准确地预测了反应产品,并确定了类似药物的分子中的反应位点.
科学领域:
- 有机化学 有机化学
- 计算化学的计算化学
- 药用化学 医学化学
背景情况:
- 由于多功能下游合反应,C-H玻利化对于合成制药候选物至关重要.
- 在C-H玻利化中预测区域选择性,特别是对具有异环的复杂药物样分子,仍然是一个重大挑战.
研究的目的:
- 评估一种语言模型T5Chem的有效性,用于预测C-H玻利化反应结果.
- 评估模型在产品生成和化反应现场反应性分类中的性能.
主要方法:
- 利用了来自Reaxys的玻利化反应数据集.
- 微调了一个T5Chem多任务语言模型,最初是在USPTO_500_MT专利数据上训练的.
- 评估了模型预测反应产品和分类反应性C-H键的能力.
主要成果:
- 微调的T5Chem模型在生成正确的C-H玻利化产品时达到79%的准确性.
- 该模型在分类反应性芳香C-H键方面表现出很高的性能,准确率为95%,预测值为88%.
- 性能超过了基于图形的专用神经网络.
结论:
- 语言模型,特别是T5Chem,为预测C-H化区域选择性提供了强大而准确的方法.
- 这种计算策略可以帮助有效设计和合成制药化合物.
- 该模型的预测能力超过了当前基于图形的方法,为改进的反应预测工具铺平了道路.
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