Relevance and Potential Applications of C2‐Carboxylated 1,3‐Azoles DOI Creative Commons
Kerrin Janssen, Johannes Kirchmair, Jonny Proppe

et al.

ChemMedChem, Journal Year: 2024, Volume and Issue: 19(21)

Published: July 18, 2024

Abstract Carbon dioxide (CO 2 ) is an economically viable and abundant carbon source that can be incorporated into compounds such as C2‐carboxylated 1,3‐azoles relevant to the pharmaceutical, cosmetics, pesticide industries. Of 2.4 million commercially available C2‐unsubstituted 1,3‐azole compounds, less than 1 % are currently purchasable their derivatives, highlighting substantial gap in compound availability. This availability leaves ample opportunities for exploring synthetic accessibility use of carboxylated azoles bioactive compounds. In this study, we analyze quantify relevance small‐molecule research. An analysis molecular databases ZINC, ChEMBL, COSMOS, DrugBank identified anticoagulant aroma‐giving Moreover, a pharmacophore highlights promising pharmaceutical potential associated with 1,3‐azoles, revealing ATP‐sensitive inward rectifier potassium channel (K ATP Kinesin‐like protein KIF18 A targets potentially addressed 1,3‐azoles. several bioisosteres conclusion, further exploration chemical space recommended harness full drug discovery related fields.

Language: Английский

Machine-Learning-Guided Discovery of Electrochemical Reactions DOI Creative Commons
Andrew F. Zahrt, Yiming Mo, Kakasaheb Y. Nandiwale

et al.

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(49), P. 22599 - 22610

Published: Dec. 2, 2022

The molecular structures synthesizable by organic chemists dictate the functions they can create. invention and development of chemical reactions are thus critical for to access new desirable functional molecules in all disciplines chemistry. This work seeks expedite exploration emerging areas chemistry devising a machine-learning-guided workflow reaction discovery. Specifically, this study uses machine learning predict competent electrochemical reactions. To end, we first develop representation that enables production general models with limited training data. Next, employ automated experimentation test large number These categorized as or incompetent mixtures, classification model was trained competency. is used screen 38,865 potential silico, predictions identify synthetic mechanistic interest, 80% which found be competent. Additionally, provide 38,865-member set hope accelerating field. We envision adopting such could enable rapid many fields

Language: Английский

Citations

54
%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

37
Recent advances in copper-catalyzed decarboxylative reactions of propargylic cyclic carbonates/carbamates DOI
Yong You, Yanping Zhang, Zhen‐Hua Wang

et al.

Chemical Communications, Journal Year: 2023, Volume and Issue: 59(49), P. 7483 - 7505

Published: Jan. 1, 2023

Copper-catalyzed decarboxylative reactions are powerful strategies for the construction of widely available skeletons such as allenes, ethynyl-containing heterocycles, and quaternary carbon centers.

Language: Английский

Citations

26
A unified ML framework for solubility prediction across organic solvents DOI Creative Commons
Antony D. Vassileiou, Murray N. Robertson,

Bruce G. Wareham

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 2(2), P. 356 - 367

Published: Jan. 1, 2023

A generic framework for enhancing an initial solubility prediction with ML, even simple methods and a modestly sized, sparse dataset. We dissect the setup to show model “locking on” target system as more data are made available.

Language: Английский

Citations

25
Catalytic Enantioselective Intramolecular Oxa-Michael Reaction to α,β-Unsaturated Esters and Amides DOI Creative Commons
Guanglong Su, Michele Formica, Ken Yamazaki

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(23), P. 12771 - 12782

Published: May 30, 2023

A bifunctional iminophosphorane (BIMP)-catalyzed, enantioselective intramolecular oxa-Michael reaction of alcohols to tethered, low electrophilicity Michael acceptors is described. Improved reactivity over previous reports (1 day vs 7 days), excellent yields (up 99%), and enantiomeric ratios 99.5:0.5 er) are demonstrated. The broad scope, enabled by catalyst modularity tunability, includes substituted tetrahydrofurans (THFs) tetrahydropyrans (THPs), oxaspirocycles, sugar natural product derivatives, dihydro-(iso)-benzofurans, iso-chromans. state-of-the-art computational study revealed that the enantioselectivity originates from presence several favorable intermolecular hydrogen bonds between BIMP substrate induce stabilizing electrostatic orbital interactions. newly developed catalytic approach was carried out on multigram scale, multiple adducts were further derivatized an array useful building blocks, providing access enantioenriched biologically active molecules products.

Language: Английский

Citations

25
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

12
Predicting relative efficiency of amide bond formation using multivariate linear regression DOI Creative Commons
Brittany C. Haas, Adam E. Goetz, Ana Bahamonde

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2022, Volume and Issue: 119(16)

Published: April 11, 2022

Significance Given the ubiquity of amide coupling reactions, understanding factors which influence success reaction and having means to predict rate would streamline synthetic efforts. This study outlines a data science–based workflow for effective statistical modeling with sparse experimental data. We demonstrated informed substrate selection, collection interpretable molecular descriptors, model development rates. The resulting models illuminate features that impact allow prediction untested

Language: Английский

Citations

36
A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors DOI Creative Commons

Jingru Lu,

Irina Paci, David C. Leitch

et al.

Chemical Science, Journal Year: 2022, Volume and Issue: 13(43), P. 12681 - 12695

Published: Jan. 1, 2022

A model for S N Ar reactivity is reported, built from relative rate data obtained by competition studies. Based only on molecular descriptors of the electrophile, predicts and site selectivity many complex substrates.

Language: Английский

Citations

27
Prediction of Nucleophilicity and Electrophilicity Based on a Machine‐Learning Approach DOI
Yidi Liu, Qi Yang,

Junjie Cheng

et al.

ChemPhysChem, Journal Year: 2023, Volume and Issue: 24(14)

Published: May 3, 2023

Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) (E), which proved to be useful tool rationalization chemical reactivity. this study, holistic prediction model was developed through machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical solvent features, purpose. With 1115 nucleophiles, 285 electrophiles, 22 solvents, dataset is currently largest one prediction. The rSPOC trained Extra Trees algorithm showed high accuracy in predicting Mayr's N E parameters R2 0.92 0.93, MAE 1.45 1.45, respectively. Furthermore, practical applications model, instance, NADH, NADPH series enamines potential molecules unknown within seconds. An online platform (http://isyn.luoszgroup.com/) constructed based on current available free scientific community.

Language: Английский

Citations

17
Reactivity Prediction of Cu-Catalyzed Halogen Atom Transfer Reactions Using Data-Driven Techniques DOI
Francesca Lorandi, Marco Fantin, Hossein Jafari

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(39), P. 21587 - 21599

Published: Sept. 21, 2023

In catalysis, linear free energy relationships (LFERs) are commonly used to identify reaction descriptors that enable the prediction of outcomes and design more effective catalysts. Herein, LFERs established for reductive cleavage C(sp3)-X bond in alkyl halides (RX) by Cu complexes. This represents activation step atom transfer radical polymerization addition/cyclization. The values rate constant, kact, 107 complex/RX couples 5 different solvents spanning over 13 orders magnitude were effectively interpolated equation: log kact = sC(I + C S), where I, C, S are, respectively, initiator, catalyst, solvent parameters, sC is catalyst-specific sensitivity parameter. Furthermore, each these parameters was correlated relevant descriptors, which included dissociation RX its Tolman cone angle θ, electron affinity X, stabilization energy, standard reduction potential complex, polarizability parameter π* solvent, distortion complex transition state. set establishes fundamental properties complexes determine their reactivity need be considered when designing novel systems reactions. Finally, a multivariate regression (MLR) approach adopted develop an objective model surpassed predictive capability LFER equation. Thus, MLR employed predict >2000 pairs.

Language: Английский

Citations

17