Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions

Highly active catalysts are required in numerous industrial fields; therefore, to minimize costs and development time, catalyst design using machine learning has attracted significant attention. This study focused on a reaction system where two types of cross-coupling reactions, namely, Buchwald-Hartwig type cross-coupling (BHCC) and Suzuki-Miyaura type cross-coupling (SMCC) reactions, occur simultaneously. Constructing a machine-learning model that considers all experimental conditions is essential to accurately predict the product yield for both the BHCC and the SMCC reactions. The objective of this study was to establish explanatory variables x that considered all experimental conditions within the reaction system involving simultaneous cross-couplings and to design catalysts that achieve the target yield and the development of novel reactions. To accomplish this, Bayesian optimization was combined with established variables x to design new catalysts and enhance reaction selectivity. Moreover, the catalyst design in this study successfully pioneered new reactions involving Cu, Rh, and Pt catalysts in a reaction system that did not previously react with transition metals other than Ni or Pd.

Palladium-Catalyzed Synthesis of Heterocyclic Ring Systems by Combination of Regioselective C–C with Twofold C–N Couplings

The combination of regioselective palladium-catalyzed C–C with twofold C–N couplings allows for the synthesis of a variety of heterocyclic ring systems. Starting materials include thiophenes and benzothiophenes, pyrroles and indoles, furans and benzofurans, pyridines, quinolines and quinoxalines, complex heterocyclic systems and benzophenone derivatives. The products are in many cases complex polyheterocyclic systems, which are not readily available by other methods or, in a number of cases, were not described in the literature before. They are of pharmacological relevance or interesting in the field of material science. Products include thieno[3,2-b:4,5-b′]diindoles, thieno[3,2-b]indoles, thieno[3,4-b]indoles, 5,10-dihydroindolo[3,2-b]indoles, furo[3,2-b:4,5-b′]diindoles, benzo[4,5]furo[3,2-b]indoles, 5,7-dihydropyrido[3,2-b:5,6-b′]diindoles, 5,7-dihydropyrido[2,3-b:6,5-b´]diindoles, α-, β-, γ- and δ-carbolines, indolo[3,2-b]quinolines, indolo[2,3-b]quinolines, indolo[3,2-c]quinolines, indoloquinoxalines, pyrido[2′,1′:2,3]imidazo[4,5-b]indoles, thiadiazolo[2′,3′:2,3]imidazo[4,5-b]indoles, benzo[b]carbazolediones, acridones and thieno[3,2-b]quinolones.

Contents

1 Introduction

2 Thiophenes and Benzothiophenes

3 Pyrroles and Indoles

4 Furans and Benzofurans

5 Pyridines

6 Quinolines and Quinoxalines

7 Complex Heterocyclic Systems

8 2,3-Dibromonaphthoquinone

9 Benzophenone Derivatives

10 Conclusions

Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts

To improve product yields in synthetic reactions, it is important to use appropriate catalysts. In this study, we used machine learning to design catalysts for a reaction system in which both Buchwald-Hartwig-type and Suzuki-Miyaura-type cross-coupling reactions proceed simultaneously. First, using an existing dataset, yield prediction models were constructed with machine learning between experimental conditions, including the substrate and catalyst and the yields of the two products. Seven methods for calculating both the substrate and catalyst descriptors were proposed, and the predictive ability of the yield prediction models was discussed in terms of the descriptors and machine learning methods. Then, the constructed models were used to predict the compound yields for new combinations of substrates and catalysts, and the predictions were experimentally validated with high reproducibility, confirming that machine learning can predict yields from experimental conditions with high accuracy. In addition, to design catalysts that will improve the yields in our dataset, we added datasets collected from scientific papers and designed catalyst ligands. The proposed catalyst candidates were tested in actual synthetic experiments, and the experimental results exceeded the existing yields.