Dataset Information

Cooperative Effects in Switchable Catalysis: Enhancing Double-Click Reaction Yield of Symmetrical Rotaxanes.

ABSTRACT: Reversible switching between the closed cyclic dimeric assembly [Cu₂ (1)₂ ]²⁺ (OFF state) and the extended dimeric homoleptic complex [FeCu₂ (1)₂ ]⁴⁺ (ON State) by addition/removal of Fe²⁺ triggered catalysis of a double-click reaction and high yield preparation of [2]rotaxanes. Mechanistic and computational studies provide valuable general insight for double-click strategies by revealing cooperative effects in the second cycloaddition step due to a distance-tolerant preorganization of the first-click product by the two copper(I)-loaded phenanthroline subunits of [FeCu₂ (1)₂ ]⁴⁺ .

SUBMITTER: Ghosh A

PROVIDER: S-EPMC8457069 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cooperative Effects in Switchable Catalysis: Enhancing Double-Click Reaction Yield of Symmetrical Rotaxanes.

Ghosh Amit A Paul Indrajit I Schmittel Michael M

Angewandte Chemie (International ed. in English) 20210807 37

Reversible switching between the closed cyclic dimeric assembly [Cu2 (1)2 ]2+ (OFF state) and the extended dimeric homoleptic complex [FeCu2 (1)2 ]4+ (ON State) by addition/removal of Fe2+ triggered catalysis of a double-click reaction and high yield preparation of [2]rotaxanes. Mechanistic and computational studies provide valuable general insight for double-click strategies by revealing cooperative effects in the seco ...[more]

PMID: 34289211

Similar Datasets

Project description:Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of reliable automated pathway evaluation tools. As a critical metric for evaluating chemical reactions, accurate prediction of reaction yields helps improve the practicality of DASP algorithms in the real-world scenarios. Currently, accurately predicting yields of interesting reactions still faces numerous challenges, mainly including the absence of high-quality generic reaction yield datasets and robust generic yield predictors. To compensate for the limitations of high-throughput yield datasets, we curated a generic reaction yield dataset containing 12 reaction categories and rich reaction condition information. Subsequently, by utilizing 2 pretraining tasks based on chemical reaction masked language modeling and contrastive learning, we proposed a powerful bidirectional encoder representations from transformers (BERT)-based reaction yield predictor named Egret. It achieved comparable or even superior performance to the best previous models on 4 benchmark datasets and established state-of-the-art performance on the newly curated dataset. We found that reaction-condition-based contrastive learning enhances the model's sensitivity to reaction conditions, and Egret is capable of capturing subtle differences between reactions involving identical reactants and products but different reaction conditions. Furthermore, we proposed a new scoring function that incorporated Egret into the evaluation of multistep synthesis routes. Test results showed that yield-incorporated scoring facilitated the prioritization of literature-supported high-yield reaction pathways for target molecules. In addition, through meta-learning strategy, we further improved the reliability of the model's prediction for reaction types with limited data and lower data quality. Our results suggest that Egret holds the potential to become an essential component of the next-generation DASP tools.

Dataset Information

Cooperative Effects in Switchable Catalysis: Enhancing Double-Click Reaction Yield of Symmetrical Rotaxanes.

Publications

Cooperative Effects in Switchable Catalysis: Enhancing Double-Click Reaction Yield of Symmetrical Rotaxanes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets