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

Applying statistical modeling strategies to sparse datasets in synthetic chemistry DOI Creative Commons
Brittany C. Haas, Dipannita Kalyani, Matthew S. Sigman

et al.

Science Advances, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and predictive tool many optimization objectives. This review aimed tutorial those entering the area chemistry. We provide case studies to highlight considerations approaches that can be used successfully analyze datasets low data regimes, common situation encountered given experimental demands Statistical hinges on (what being modeled), descriptors (how are represented), algorithms modeled). Herein, we focus how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, turnover number) structures binned, heavily skewed, distributed) influence choice algorithm constructing chemically insightful models.

Language: Английский

Citations

3

Calculating the Precision of Student-Generated Datasets Using RStudio DOI
Joseph Chiarelli, Melissa A. St. Hilaire, Brandi L. Baldock

et al.

Journal of Chemical Education, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

Language: Английский

Citations

1

Recommending reaction conditions with label ranking DOI Creative Commons
Eunjae Shim, Ambuj Tewari, Tim Cernak

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Label ranking is introduced as a conceptually new means for prioritizing experiments. Their simplicity, ease of application, and the use aggregation facilitate their ability to make accurate predictions with small datasets.

Language: Английский

Citations

1

Transfer learning across different photocatalytic organic reactions DOI Creative Commons
Naoki Noto,

Ryuga Kunisada,

Tabea Rohlfs

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 10, 2025

Language: Английский

Citations

1

EnTdecker − A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis DOI
Leon Schlosser,

Debanjan Rana,

Philipp M. Pflüger

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(19), P. 13266 - 13275

Published: May 2, 2024

Due to the magnitude of chemical space, discovery novel substrates in energy transfer (EnT) catalysis remains a daunting task. Experimental and computational strategies identify compounds that successfully undergo EnT-mediated reactions are limited by their time cost efficiency. To accelerate process EnT catalysis, we herein present EnTdecker platform, which facilitates large-scale virtual screening potential using machine-learning (ML) based predictions excited state properties. achieve this, data set is created containing more than 34,000 molecules aiming cover vast fraction synthetically relevant compound space for catalysis. Using this predictive models trained, aptitude an in-lab application demonstrated rediscovering successful from literature as well experimental validation through luminescence-based screening. By reducing effort needed obtain properties, platform represents tool efficiently guide substrate selection increase success rate Moreover, easy-to-use web application, made publicly accessible under entdecker.uni-muenster.de.

Language: Английский

Citations

9

Accelerating the Development of Sustainable Catalytic Processes through Data Science DOI
Jason M. Stevens, Jacob M. Ganley, Matthew J. Goldfogel

et al.

Organic Process Research & Development, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

Citations

1

Multi‐Threshold Analysis for Chemical Space Mapping of Ni‐Catalyzed Suzuki‐Miyaura Couplings DOI

Austin LeSueur,

Nari Tao,

Abigail G. Doyle

et al.

European Journal of Organic Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 11, 2024

Abstract A key challenge in synthetic chemistry is the selection of high‐performing ligands for cross‐coupling reactions. To address this challenge, work presents a classification workflow to identify physicochemical descriptors that bin monophosphine as active or inactive Ni‐catalyzed Suzuki‐Miyaura coupling Using five previously published high‐throughput experimentation datasets training, we found binary classifier using phosphine's minimum buried volume and Boltzmann‐averaged electrostatic potential most effective at distinguishing high low‐yielding ligands. Experimental validations are also presented. two from represent chemical space leads more predictive guide structure‐reactivity relationships compared with classic representations.

Language: Английский

Citations

5

COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets DOI Creative Commons
Zhen Cao, Laura Falivene, Albert Poater

et al.

Cell Reports Physical Science, Journal Year: 2024, Volume and Issue: unknown, P. 102348 - 102348

Published: Dec. 1, 2024

Language: Английский

Citations

5

Photocatalyzed Azidofunctionalization of Alkenes via Radical‐Polar Crossover DOI Open Access
Pierre Palamini, Alexandre A. Schoepfer, Jérôme Waser

et al.

Angewandte Chemie International Edition, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

The azidofunctionalization of alkenes under mild conditions using commercially available starting materials and easily accessible reagents is reported based on a radical-polar crossover strategy. A broad range alkenes, including vinyl arenes, enamides, enol ethers, sulfides, dehydroamino esters, were regioselectively functionalized with an azide nucleophiles such as azoles, carboxylic acids, alcohols, phosphoric oximes, phenols. method led to more efficient synthesis 1,2-azidofunctionalized pharmaceutical intermediates when compared previous approaches, resulting in both reduction step count increase overall yield. scope limitations these transformations further investigated through standard unbiased selection 15 substrate combinations out 1,175,658 possible clustering technique.

Language: Английский

Citations

0

Photocatalyzed Azidofunctionalization of Alkenes via Radical‐Polar Crossover DOI Open Access
Pierre Palamini, Alexandre A. Schoepfer, Jérôme Waser

et al.

Angewandte Chemie, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Abstract The azidofunctionalization of alkenes under mild conditions using commercially available starting materials and easily accessible reagents is reported based on a radical‐polar crossover strategy. A broad range alkenes, including vinyl arenes, enamides, enol ethers, sulfides, dehydroamino esters, were regioselectively functionalized with an azide nucleophiles such as azoles, carboxylic acids, alcohols, phosphoric oximes, phenols. method led to more efficient synthesis 1,2‐azidofunctionalized pharmaceutical intermediates when compared previous approaches, resulting in both reduction step count increase overall yield. scope limitations these transformations further investigated through standard unbiased selection 15 substrate combinations out 1,175,658 possible clustering technique.

Language: Английский

Citations

0