Intermediate Knowledge Enhanced the Performance of N-Acylation Yield Prediction Model DOI Creative Commons
Chonghuan Zhang,

Qianghua Lin,

Hao Deng

et al.

Published: Aug. 16, 2024

Acylation is an important reaction widely applied in medicinal chemistry. However, yield optimization remains a challenging issue due to the broad conditions space. Recently, accurate condition recommendations via machine learning have emerged as novel and efficient method achieve desired transformations without trial-and-error process. Nonetheless, accurately predicting yields complex relationships involved. Herein, we present our strategy address this problem. Two steps were taken ensure quality of dataset. First, skillfully selected substrates diversity representativeness. Second, experiments conducted using in-house high-throughput experimentation (HTE) platform minimize influence human factors. Additionally, proposed intermediate knowledge-embedded enhance model’s robustness. The performance model was first evaluated at three different levels—random split, partial substrate novelty, full novelty. All metrics these cases improved dramatically, achieving R2 0.89, MAE 6.1%, RMSE 8.0%. Moreover, generalization assessed external datasets from reported literature. prediction error for nine reactions among 30 less than 5%, able identify which pair with reactivity cliff had higher yield. In summary, research demonstrated feasibility predictions through combination HTE embedding knowledge into model. This approach also has potential facilitate other related tasks.

Language: Английский

Recent Developments in Stereoselective Reactions of Sulfoxonium Ylides DOI Creative Commons

Ciarán O’Shaughnessy,

Mukulesh Mondal,

Nessan J. Kerrigan

et al.

Molecules, Journal Year: 2025, Volume and Issue: 30(3), P. 655 - 655

Published: Feb. 1, 2025

This review probes the recent developments in stereoselective reactions within area of sulfoxonium ylide chemistry since early 2000s. An abundance research has been applied to its emergence 1960s. There a continued effort then with work traditional areas, such as epoxidation, aziridination and cyclopropanation. Efforts have also novel olefination insertion reactions, develop methodologies using organocatalysis transition metal catalysis. The growing interrupted Johnson–Corey–Chaykovsky is described, whereby unexpected cyclopropanation epoxidation developed. In general, most observed mechanistic pathway ylides formal cycloaddition: (2 + 1) (e.g., epoxides, cyclopropanes, aziridines), (3 oxetanes, azetidines), (4 indanones, indolines). involves formation zwitterionic intermediate through nucleophilic addition carbanion an electrophilic site. intramolecular cyclization occurs, constructing cyclic product. Insertion X–H bonds X = S, N or P) are observed, protonation followed by X, form inserted

Language: Английский

Citations

0
Top 20 Influential AI-Based Technologies in Chemistry DOI Creative Commons
Valentine P. Ananikov

Published: April 12, 2024

The beginning and ripening of digital chemistry is analyzed focusing on the role artificial intelligence (AI) in an expected leap chemical sciences to bring this area next evolutionary level. analytic description selects highlights top 20 AI-based technologies 7 broader themes that are reshaping field. It underscores integration tools such as machine learning, big data, twins, Internet Things (IoT), robotic platforms, smart control processes, virtual reality blockchain, among many others, enhancing research methods, educational approaches, industrial practices chemistry. significance study lies its focused overview how these innovations foster a more efficient, sustainable, innovative future sciences. This article not only illustrates transformative impact but also draws new pathways chemistry, offering broad appeal researchers, educators, industry professionals embrace advancements for addressing contemporary challenges

Language: Английский

Citations

2
Reactions with sulfoxonium ylides using metal-catalysis DOI
Marcio Hayashi, Viktor Saraiva Câmara,

Cristhian S. Oliveira

et al.

Advances in organometallic chemistry, Journal Year: 2024, Volume and Issue: unknown, P. 227 - 286

Published: Jan. 1, 2024

Language: Английский

Citations

0
Intermediate Knowledge Enhanced the Performance of N-Acylation Yield Prediction Model DOI Creative Commons
Chonghuan Zhang,

Qianghua Lin,

Hao Deng

et al.

Published: Aug. 16, 2024

Acylation is an important reaction widely applied in medicinal chemistry. However, yield optimization remains a challenging issue due to the broad conditions space. Recently, accurate condition recommendations via machine learning have emerged as novel and efficient method achieve desired transformations without trial-and-error process. Nonetheless, accurately predicting yields complex relationships involved. Herein, we present our strategy address this problem. Two steps were taken ensure quality of dataset. First, skillfully selected substrates diversity representativeness. Second, experiments conducted using in-house high-throughput experimentation (HTE) platform minimize influence human factors. Additionally, proposed intermediate knowledge-embedded enhance model’s robustness. The performance model was first evaluated at three different levels—random split, partial substrate novelty, full novelty. All metrics these cases improved dramatically, achieving R2 0.89, MAE 6.1%, RMSE 8.0%. Moreover, generalization assessed external datasets from reported literature. prediction error for nine reactions among 30 less than 5%, able identify which pair with reactivity cliff had higher yield. In summary, research demonstrated feasibility predictions through combination HTE embedding knowledge into model. This approach also has potential facilitate other related tasks.

Language: Английский

Citations

0