Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions DOI Creative Commons

Jingru Lu,

David C. Leitch

Published: Aug. 16, 2023

With the advent of high-throughput methods for both computation and experimentation, data-rich approaches to discovering understanding chemical reactions are becoming ever more central catalysis research. Organopalladium is at forefront these new approaches, providing a rich proving ground method development validation. This critical Perspective discusses number recent case studies from academic industrial laboratories that illustrate how generate, analyze, correlate large data sets quantitative predictions reactivity selectivity. Both power potential pitfalls discussed, as opportunities practical fundamental mechanistic insights.

Language: Английский

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery DOI Creative Commons
Zhengkai Tu, Thijs Stuyver,

Connor W. Coley

et al.

Chemical Science, Journal Year: 2022, Volume and Issue: 14(2), P. 226 - 244

Published: Nov. 28, 2022

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Language: Английский

Citations

85
Dataset Design for Building Models of Chemical Reactivity DOI Creative Commons
Priyanka Raghavan, Brittany C. Haas, Madeline E. Ruos

et al.

ACS Central Science, Journal Year: 2023, Volume and Issue: 9(12), P. 2196 - 2204

Published: Dec. 8, 2023

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development new synthetic processes via, for example, evaluating hypothetical reaction conditions or silico substrate tolerance. Perhaps most determining factor is composition training data whether it sufficient to train model that make accurate predictions over full domain interest. Here, we discuss design datasets ways are conducive data-driven modeling, emphasizing idea set diversity generalizability rely on choice molecular representation. We additionally experimental constraints associated with generating common types chemistry how these considerations should influence dataset building.

Language: Английский

Citations

42
Rapid planning and analysis of high-throughput experiment arrays for reaction discovery DOI Creative Commons
Babak Mahjour, Rui Zhang, Yuning Shen

et al.

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

Published: July 3, 2023

High-throughput experimentation (HTE) is an increasingly important tool in reaction discovery. While the hardware for running HTE chemical laboratory has evolved significantly recent years, there remains a need software solutions to navigate data-rich experiments. Here we have developed phactor™, that facilitates performance and analysis of laboratory. phactor™ allows experimentalists rapidly design arrays reactions or direct-to-biology experiments 24, 96, 384, 1,536 wellplates. Users can access online reagent data, such as inventory, virtually populate wells with produce instructions perform array manually, assistance liquid handling robot. After completion array, analytical results be uploaded facile evaluation, guide next series All metadata, are stored machine-readable formats readily translatable various software. We also demonstrate use discovery several chemistries, including identification low micromolar inhibitor SARS-CoV-2 main protease. Furthermore, been made available free academic 24- 96-well via interface.

Language: Английский

Citations

31
Direct C–H Hydroxylation of N-Heteroarenes and Benzenes via Base-Catalyzed Halogen Transfer DOI

Kendelyn I. Bone,

Thomas R. Puleo,

Jeffrey S. Bandar

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(14), P. 9755 - 9767

Published: March 26, 2024

Hydroxylated (hetero)arenes are valued in many industries as both key constituents of end products and diversifiable synthetic building blocks. Accordingly, the development reactions that complement address limitations existing methods for introduction aromatic hydroxyl groups is an important goal. To this end, we apply base-catalyzed halogen transfer (X-transfer) to enable direct C–H hydroxylation mildly acidic N-heteroarenes benzenes. This protocol employs alkoxide base catalyze X-transfer from sacrificial 2-halothiophene oxidants aryl substrates, forming SNAr-active intermediates undergo nucleophilic hydroxylation. Key process use 2-phenylethanol inexpensive hydroxide surrogate that, after substitution rapid elimination, provides hydroxylated arene styrene byproduct. Use simple 2-halothiophenes allows 6-membered 1,3-azole derivatives, while a rationally designed 2-halobenzothiophene oxidant extends scope electron-deficient benzene substrates. Mechanistic studies indicate reversible, suggesting deprotonation, halogenation, steps operate synergy, manifesting unique selectivity trends not necessarily dependent on most position. The utility method further demonstrated through streamlined target molecule syntheses, examples regioselectivity contrast alternative methods, scalable recycling thiophene oxidants.

Language: Английский

Citations

10
Design, Characterization, Molecular Docking, and Insecticidal Activity of Some New Heterocyclic Compounds Containing Pyrazole Moiety against Spodoptera frugiperda (J.E. Smith) (Noctuidae: Lepidoptera) DOI
M. S. A. El‐Gaby, Mohamed Hussein,

Mohamed Ahmed Mahmoud Abdel Reheim

et al.

Russian Journal of Bioorganic Chemistry, Journal Year: 2024, Volume and Issue: 50(3), P. 917 - 933

Published: June 1, 2024

Language: Английский

Citations

9
SNAr Regioselectivity Predictions: Machine Learning Triggering DFT Reaction Modeling through Statistical Threshold DOI
Yanfei Guan, Taegyo Lee, Ke Wang

et al.

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(12), P. 3751 - 3760

Published: June 5, 2023

Fast and accurate prospective predictions of regioselectivity can significantly reduce the time resources spent on unproductive transformations in pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition state (TST) machine learning (ML) methods has been widely used to predict outcomes such as selectivity. However, TST ML are either time-consuming or data-dependent. Herein, we introduce a prototype seamlessly bridging by triggering resource-intensive but much less domain-sensitive DFT calculations only confident predictions. The proposed workflow was trained tested both Pfizer internal dataset USPTO public for SNAr reactions. Our method is fast, which achieves 96.3 94.7% accuracy predicting correct major product datasets, respectively, fraction conventional computing time.

Language: Английский

Citations

12
Machine learning and DFT coupling: A powerful approach to explore organic amine catalysts for ring-opening polymerization reaction DOI

Haoliang Zhong,

Ying Wu, Xu Li

et al.

Chemical Engineering Science, Journal Year: 2024, Volume and Issue: 292, P. 119955 - 119955

Published: March 8, 2024

Language: Английский

Citations

5
Mechanisms and Site Selectivity of (Het)Ar–X Oxidative Addition to Pd(0) Are Controlled by Frontier Molecular Orbital Symmetry DOI Creative Commons

Jingru Lu,

Nathan D. Schley, Irina Paci

et al.

Published: May 28, 2024

We report how the reaction mechanism and site-selectivity of 2-halopyridine oxidative addition to L2Pd(0) are both controlled by frontier molecular orbital symmetry. Comparing rates for pairs 2-chloro-3-EDG-pyridines / 2-chloro-5-EDG-pyridines (EDG = electron-donating group: NH2, OMe F) Pd(PCy3)2 reveals 3-EDG isomers undergo ~100 times faster than their 5-EDG counterparts (∆ΔG‡OA 10.4-11.6 kJ mol-1). Experimental computational mechanistic studies reveal that LUMO symmetries substrates control mechanism. For derivatives, high coefficients at reactive C2 position, antibonding symmetry through C2=N bond pyridine lead a nucleophilic displacement oxida-tive Conversely, derivatives has node C5–C2 plane, lead-ing minimal contribution carbon. The higher energy LUMO+1 substantial density C2, but nitrogen. This leads undergoing 3-centered insertion These effects also multihalogenated pyridines, which we investigate electron-withdrawing substituents. Incorporating simple fron-tier based descriptors quantitative multivariate linear model im-proved prediction accuracy relative substituted L2Pd(0).

Language: Английский

Citations

5
Mechanisms and Site Selectivity of (Het)Ar–X Oxidative Addition to Pd(0) Are Controlled by Frontier Molecular Orbital Symmetry DOI Creative Commons

Jingru Lu,

Nathan D. Schley, Irina Paci

et al.

Published: April 1, 2024

We report how the reaction mechanism and site-selectivity of 2-halopyridine oxidative addition to L2Pd(0) are both controlled by frontier molecular orbital symmetry. Comparing rates for pairs 2-chloro-3-EDG-pyridines / 2-chloro-5-EDG-pyridines (EDG = electron-donating group: NH2, OMe F) Pd(PCy3)2 reveals 3-EDG isomers undergo ~100 times faster than their 5-EDG counterparts (∆ΔG‡OA 10.4-11.6 kJ mol-1). Experimental computational mechanistic studies reveal that LUMO symmetries substrates control mechanism. For derivatives, high coefficients at reactive C2 position, antibonding symmetry through C2=N bond pyridine lead a nucleophilic displacement oxida-tive Conversely, derivatives has node C5–C2 plane, lead-ing minimal contribution carbon. The higher energy LUMO+1 substantial density C2, but nitrogen. This leads undergoing 3-centered insertion These effects also multihalogenated pyridines, which we investigate electron-withdrawing substituents. Incorporating simple fron-tier based descriptors quantitative multivariate linear model im-proved prediction accuracy relative substituted L2Pd(0).

Language: Английский

Citations

3
Mechanisms and Site Selectivity of (Het)Ar–X Oxidative Addition to Pd(0) Are Controlled by Frontier Molecular Orbital Symmetry DOI

Jingru Lu,

Nathan D. Schley, Irina Paci

et al.

Organometallics, Journal Year: 2024, Volume and Issue: unknown

Published: July 16, 2024

Language: Английский

Citations

3