关键词: 化学反应, 神经网络, 模型, 反应图描述符, 反应评价指标设计

Abstract:

Chemical reaction selection and design plays a key role in the field of drug synthesis and material synthesis. The selection of a proper chemical reaction could greatly optimize the synthesis reaction conditions, reduce time and improve synthesis yield of the product. In order to better distinguish the advantage degree of chemical reactions, a graphical neural network model was proposed to distinguish reaction superiority by using a reaction graph descriptor based on reaction atomic mapping relationships. Firstly, a reaction dataset for distinguishing the superiority of chemical reactions was established by using USPTO data. Then, the graphical neural network modeling method was used to establish the mapping relationship between the chemical reactions molecular and atomic features and the reaction superiority probability values. Finally, the aspirin different chemical synthesis reactions were taken as examples to prove and verify the feasibility and superiority of the reaction indicator by using the relevant experimental information.

Key words: chemical reaction, neural networks, model, reaction graph descriptor, reaction indicator design