RPBP:基于副产品的深度回复合成反应预测
Yingchao Yan1, Yang Zhao1, Huifeng Yao1
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
Journal of chemical information and modeling
|September 19, 2023
概括
这项研究引入了一种新的AI框架,用于预测包含副产品的逆合成. 这种方法可以提高药物发现的合成路径设计的准确性和效率.
科学领域:
- 有机化学 有机化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 逆合成预测对于在有机合成和药物发现中设计合成路径至关重要.
- 数据驱动的深度学习模型有先进的回归合成预测,但往往忽略了副产品信息.
- 现有的基准数据集缺乏全面的副产品数据,限制了模型的性能.
研究的目的:
- 开发一种新的两阶段逆合成预测框架,明确纳入副产品信息.
- 通过利用副产品数据,提高预测合成路线的准确性和效率.
- 证明拟议框架在加速药物发现方面的实际实用性.
主要方法:
- 提出了一个名为RPBP的两阶段逆合成反应预测框架.
- RPBP首先根据产品分子预测反应的副产品.
- 随后,它使用产品和预测的副产品预测反应剂,考虑试剂,条件和反应场所.
主要成果:
- 对于未知反应类,RPBP模型达到54.7%的top-1精度,对于已知的反应类达到66.6%.
- 与现有方法相比,RPBP表现出优越的性能,特别是对于已知的类反应.
- 该框架通过结合副产品信息,在自然语言处理 (NLP) 任务中减少了模型学习的复杂性.
结论:
- 整合副产品信息显著提高了回复合成预测的准确性.
- RPBP框架为加速药物发现和有机合成提供了一种实用和有效的方法.
- 该模型从文献中预测酶药物的能力突出显示了其对现实世界的应用的潜力.
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