Generation of novel Diels–Alder reactions using a generative adversarial network. Issue 52 (25th November 2022)

• Record Type: Journal Article
• Authors: Li, Sheng; Wang, Xinqiao; Wu, Yejian; Duan, Hongliang; Tang, Lan
• Is Part Of: RSC advances. Volume 12:Issue 52(2022)
• Page Start: 33801
• Page End: 33807
• Publication Date: 2022-11-25
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d2ra06022a
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : We obtained 1441 novel reactions by using a generative adversarial network for reaction generation. Abstract : Deep learning has enormous potential in the chemical and pharmaceutical fields, and generative adversarial networks (GANs) in particular have exhibited remarkable performance in the field of molecular generation as generative models. However, their application in the field of organic chemistry has been limited; thus, in this study, we attempt to utilize a GAN as a generative model for the generation of Diels–Alder reactions. A MaskGAN model was trained with 14 092 Diels–Alder reactions, and 1441 novel Diels–Alder reactions were generated. Analysis of the generated reactions indicated that the model learned several reaction rules in-depth. Thus, the MaskGAN model can be used to generate organic reactions and aid chemists in the exploration of novel reactions.