SadPhos Library: A Comprehensive Resource for Exploring Chiral Ligand Chemical Space DOI
Shuang Yu

Chemistry - An Asian Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract Traditionally, the discovery of ligands for organic reactions has relied heavily on intuition and experience chemists, leading to a trial‐and‐error process that is both time‐consuming inherently biased. The rise data science now offers more systematic efficient approach exploring chemical spaces, moving beyond heuristic constraints conventional ligand design enabling data‐driven, predictive method. In this study, we introduce “SadPhos Library”, comprehensive collection 890 reported chiral sulfinamide phosphine ligands, use physical descriptors systematically map their space. By examining small dataset known active demonstrate how SadPhos library can help identify key properties associated with performance thus streamline optimization. Furthermore, employing dimensionality reduction clustering techniques, pinpoint representative subset facilitates targeted exploration diverse landscape.

Language: Английский

Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning DOI
Li‐Cheng Xu, Johanna Frey, Xiaoyan Hou

et al.

Nature Synthesis, Journal Year: 2023, Volume and Issue: 2(4), P. 321 - 330

Published: Jan. 30, 2023

Language: Английский

Citations

48
Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

Citations

35
%VBur index and steric maps: from predictive catalysis to machine learning DOI Creative Commons
Sílvia Escayola, Naeimeh Bahri‐Laleh, Albert Poater

et al.

Chemical Society Reviews, Journal Year: 2023, Volume and Issue: 53(2), P. 853 - 882

Published: Dec. 19, 2023

Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups molecules.

Language: Английский

Citations

35
Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge DOI Creative Commons

Shu-Wen Li,

Li‐Cheng Xu, Cheng Zhang

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: June 15, 2023

Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to high-dimensional relationship between molecular structure function, it is challenging achieve predictive modelling transformation with required extrapolative ability chemical interpretability. To meet gap rich domain knowledge chemistry advanced graph model, herein we report a knowledge-based model that embeds digitalized steric electronic information. In addition, interaction module developed enable learning synergistic influence reaction components. this study, demonstrate achieves excellent predictions yield stereoselectivity, whose corroborated by additional scaffold-based data splittings experimental verifications new catalysts. Because embedding local environment, allows atomic level interpretation on overall performance, which serves as useful guide engineering towards target function. This offers an interpretable approach performance prediction, pointing out importance knowledge-constrained purpose.

Language: Английский

Citations

26
AI for organic and polymer synthesis DOI

Hong Xin,

Qi Yang, Kuangbiao Liao

et al.

Science China Chemistry, Journal Year: 2024, Volume and Issue: 67(8), P. 2461 - 2496

Published: June 26, 2024

Language: Английский

Citations

9
Artificial Intelligence Enabled Biomineralization for Eco‐Friendly Nanomaterial Synthesis: Charting Future Trends DOI Creative Commons

Vaisali Chandrasekar,

Anu Jayanthi Panicker,

Ajay Vikram Singh

et al.

Nano Select, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

ABSTRACT The applications of nanoparticles (NPs) have shown tremendous growth during the last decade in field biomedicine. Although chemical and physical methods dominate large‐scale NP synthesis, such are also known for their adverse impact on environment health. In contrast, use biological systems provides a sustainable alternative producing functional NPs by biomineralization process. transformative power artificial intelligence (AI) has been proven prudent diagnosis, drug development, therapy, clinical decision‐making. AI can be utilized tailored design, scale‐up biomedical applications. present review an overview process its advantages over other eco‐friendly synthesis opportunities. Specific emphasis is provided application cancer therapy how biologically compatible improve management. Finally, to best our knowledge, potential integrating comprehensively analyzed first time. Additionally, help surpass conventionally synthesized toxicity toxicology material science provided.

Language: Английский

Citations

1
Parametrization of κ2-N,O-Oxazoline Preligands for Enantioselective Cobaltaelectro-Catalyzed C–H Activations DOI Creative Commons
Suman Dana, Neeraj Kumar Pandit, Philipp Boos

et al.

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 4450 - 4459

Published: Feb. 28, 2025

Enantioselective electrocatalyzed C–H activations have emerged as a transformative platform for the assembly of value-added chiral organic molecules. Despite recent progress, construction multiple C(sp3)-stereogenic centers via C(sp3)–C(sp3) bond formation has thus far proven to be elusive. In contrast, we herein report an annulative activation strategy, generating Fsp3-rich molecules with high levels diastereo- and enantioselectivity. κ2-N,O-oxazoline preligands were effectively employed in enantioselective cobalt(III)-catalyzed reactions. Using DFT-derived descriptors regression statistical modeling, performed parametrization study on modularity preligands. The resulted model describing ligands' selectivity characterized by key steric, electronic, interaction behaviors.

Language: Английский

Citations

1
Enantioselective C–H annulations enabled by either nickel- or cobalt-electrocatalysed C–H activation for catalyst-controlled chemodivergence DOI Creative Commons
Tristan von Münchow, Neeraj Kumar Pandit, Suman Dana

et al.

Nature Catalysis, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

Citations

1
Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis DOI Creative Commons
Shuo‐Qing Zhang, Li‐Cheng Xu,

Shu‐Wen Li

et al.

Chemistry - A European Journal, Journal Year: 2022, Volume and Issue: 29(6)

Published: Oct. 7, 2022

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential data-driven prediction synthesis performance. Digitalization and ML modelling are key strategies to fully exploit unique within synergistic interplay between experimental data robust performance selectivity. A series exciting studies demonstrated importance chemical knowledge implementation ML, improves model's capability for making predictions that challenging often go beyond abilities human beings. This Minireview summarizes cutting-edge embedding techniques model designs synthetic prediction, elaborating how can be incorporated into until June 2022. By merging organic tactics informatics, we hope this Review provide guide map intrigue chemists revisit digitalization computerization chemistry principles.

Language: Английский

Citations

28
HTE and machine learning-assisted development of iridium(i)-catalyzed selective O–H bond insertion reactions toward carboxymethyl ketones DOI

Yougen Xu,

Feixiao Ren,

Lebin Su

et al.

Organic Chemistry Frontiers, Journal Year: 2023, Volume and Issue: 10(5), P. 1153 - 1159

Published: Jan. 1, 2023

By combining HTE and machine learning technologies, an iridium( i )-catalyzed highly selective O–H bond insertion reaction of carboxylic acids sulfoxonium ylides was developed, extensive space exploration accomplished.

Language: Английский

Citations

18