Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction DOI Open Access
Giovanna S. Tâmega,

Mateus Oliveira Costa,

Ariel de Araujo Pereira

et al.

The Chemical Record, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Abstract Advancements in synthetic organic chemistry are closely related to understanding substrate and catalyst reactivities through detailed mechanistic studies. Traditional investigations labor‐intensive rely on experimental kinetic, thermodynamic, spectroscopic data. Linear free energy relationships (LFERs), exemplified by Hammett relationships, have long facilitated reactivity prediction despite their inherent limitations when using constants or incorporating comprehensive Data‐driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting interpreting mechanisms effectively handling complex multiparameter strategies. This review explores selected examples of data‐driven strategies investigating reaction mechanisms. It highlights the evolution application computational descriptors inference.

Language: Английский

Cinchona Phosphinate-Catalyzed Desymmetrization of Sulfonimidamides DOI
Benjamin List,

Margareta M. Poje

Synfacts, Journal Year: 2024, Volume and Issue: 20(06), P. 0634 - 0634

Published: May 14, 2024

Key words asymmetric catalysis - cinchona alkaloids sulfonimidamides sulfur(VI) compounds acylation desymmetrization high-throughput experimentation

Language: Английский

Citations

0
Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction DOI Open Access
Giovanna S. Tâmega,

Mateus Oliveira Costa,

Ariel de Araujo Pereira

et al.

The Chemical Record, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Abstract Advancements in synthetic organic chemistry are closely related to understanding substrate and catalyst reactivities through detailed mechanistic studies. Traditional investigations labor‐intensive rely on experimental kinetic, thermodynamic, spectroscopic data. Linear free energy relationships (LFERs), exemplified by Hammett relationships, have long facilitated reactivity prediction despite their inherent limitations when using constants or incorporating comprehensive Data‐driven modeling, which integrates cheminformatics with machine learning, offers powerful tools for predicting interpreting mechanisms effectively handling complex multiparameter strategies. This review explores selected examples of data‐driven strategies investigating reaction mechanisms. It highlights the evolution application computational descriptors inference.

Language: Английский

Citations

0