From theory to experiment: transformer-based generation enables rapid discovery of novel reactions

Abstract

Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration.

Generation of novel Diels–Alder reactions using a generative adversarial network

“…Wang et al describe a Transformer-Based reaction generation strategy, and their success rate is 2.86% aer the same data processing, our method has about 1.5% improvement. 11 As shown in Table 4, we observed a 100% probability that the model generates reactions with an ortho or para product, which is consistent with our knowledge of the regioselectivity of Diels-Alder reactions, indicating that our model exhibits excellent regioselectivity. Regioselectivity refers to the preference of a reagent to react with a functional group at a particular position over another.…”

“…Wang et al describe a Transformer-Based reaction generation strategy, and their success rate is 2.86% after the same data processing, our method has about 1.5% improvement. 11 …”

“…9 The success of generative models in molecular generation inspired researchers such as Bort et al and Wang et al to further explore reaction generative models. 10,11…”

“…9 The success of generative models in molecular generation inspired researchers such as Bort et al and Wang et al to further explore reaction generative models. 10,11 Generative models are an important class of machine learning models capable of generating new data that is not included in a training dataset and have exhibited considerable potential in image, 12 text, 13 and sound generation 14 in the past few years. Among several generative deep learning models, GAN has attracted considerable attention from researchers and was rst proposed by Goodfellow et al as a novel framework for the estimation of generative models in adversarial processes.…”

Learning organo‐transition metal catalyzed reactions by graph neural networks

“…Besides the neural network models, other prediction models have been reported for single or a few categories of OMCRs to find the best reaction conditions. [8][9][10][11][12][13] Some of these predictions have been verified by experiments. However, few models can be applied to a wide variety of OMCRs because of their complexity.…”