Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme DOI

P. D. Varuna S. Pathirage,

Brody Quebedeaux,

Shahzad Akram

et al.

The Journal of Physical Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Machine learning has recently been introduced into the arsenal of tools that are available to computational chemists. In past few years, we have seen an increase in applicability these on a plethora applications, including automated exploration large fraction chemical space, reduction repetitive tasks, detection outliers databases, and acceleration molecular simulations. An attractive application machine electronic structure theory is "recycling" wave functions for faster more accurate completion complex quantum calculations. Along lines, developed hybrid chemical/machine workflows utilize information from low-level prediction higher-level functions. The data-driven coupled-cluster (DDCC) family methods discussed this article together with importance inclusion physical properties such workflows. After short introduction philosophy capabilities DDCC, present our recent progress extending its larger structures data sets. A significant advantage offered by DDCC transferability, respect different systems excitation levels. As show here, predicted at singles doubles level can be used perturbative triples CCSD(T) scheme. We conclude some personal considerations future directions related development next generation models.

Language: Английский

Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry DOI Creative Commons
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan

et al.

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(1), P. 100049 - 100049

Published: Jan. 19, 2024

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI's pivotal roles field organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies planning, accelerates catalyst discovery, and fuels material innovation so on. It seamlessly integrates data-driven algorithms with intuition to redefine As chemistry advances, it promises accelerated research, sustainability, innovative solutions chemistry's pressing challenges. The fusion poised shape field's future profoundly, offering new horizons precision efficiency. encapsulates transformation marking moment where data converge revolutionize world molecules.

Language: Английский

Citations

25
Predicting success in Cu-catalyzed C–N coupling reactions using data science DOI Creative Commons
Mohammad H. Samha, Lucas J. Karas, David B. Vogt

et al.

Science Advances, Journal Year: 2024, Volume and Issue: 10(3)

Published: Jan. 17, 2024

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads the evolving landscape of synthetic chemistry. A discipline-wide goal development workflows that integrate computational chemistry data tools with high-throughput experimentation as it provides experimentalists ability to maximize success expensive campaigns. Here, we report an end-to-end data-driven process effectively predict how structural features coupling partners ligands affect Cu-catalyzed C–N reactions. The established workflow underscores limitations posed by substrates while also providing systematic ligand prediction tool uses probability assess when will be successful. This platform strategically designed confront intrinsic unpredictability frequently encountered deployment.

Language: Английский

Citations

19
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
Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides DOI
Lucy van Dijk, Brittany C. Haas, Ngiap‐Kie Lim

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(38), P. 20959 - 20967

Published: Sept. 1, 2023

New methods for the general asymmetric synthesis of sulfonimidamides are great interest due to their applications in medicinal chemistry, agrochemical discovery, and academic research. We report a palladium-catalyzed cross-coupling method enantioselective aryl-carbonylation sulfonimidamides. Using data science techniques, virtual library calculated bisphosphine ligand descriptors was used guide reaction optimization by effectively sampling catalyst chemical space. The optimized conditions identified using this approach provided desired product excellent yield enantioselectivity. As next step, science-driven strategy also explore diverse set aryl heteroaryl iodides, providing key information about scope limitations method. Furthermore, we tested range racemic compatibility coupling partner. developed offers efficient accessing enantioenriched sulfonimidamides, which should facilitate application industrial settings.

Language: Английский

Citations

36
Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation DOI
Eike Caldeweyher, Masha Elkin, Golsa Gheibi

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(31), P. 17367 - 17376

Published: July 31, 2023

The borylation of aryl and heteroaryl C–H bonds is valuable for the site-selective functionalization in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze bond that most acidic least sterically hindered an arene, but predicting site molecules containing multiple arenes difficult. To address this challenge, we report a hybrid computational model predicts Site Borylation (SoBo) SoBo combines density functional theory, semiempirical quantum mechanics, cheminformatics, linear regression, machine learning to predict selectivity extrapolate these predictions new chemical space. Experimental validation showed major pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating will be useful guide experiments specific C(sp2)–H during development.

Language: Английский

Citations

35
Paving the road towards automated homogeneous catalyst design DOI Creative Commons
Adarsh V. Kalikadien,

A.H. Mirza,

Aydin Najl Hossaini

et al.

ChemPlusChem, Journal Year: 2024, Volume and Issue: 89(7)

Published: Jan. 26, 2024

In the past decade, computational tools have become integral to catalyst design. They continue offer significant support experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning garnered considerable attention their expansive capabilities. This Perspective provides an overview of diverse initiatives in realm design introduces our automated tailored high-throughput silico exploration chemical space. While valuable insights are gained through methods analysis space, degree automation modularity key. We argue that integration data-driven, modular workflows is key enhancing homogeneous on unprecedented scale, contributing advancement research.

Language: Английский

Citations

12
Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning DOI Creative Commons
S. Macé, Yingjian Xu, Bao N. Nguyen

et al.

ChemCatChem, Journal Year: 2024, Volume and Issue: 16(10)

Published: Jan. 5, 2024

Abstract Significant progress has been made in recent years the use of AI and Machine Learning (ML) for catalyst discovery optimisation. The effectiveness ML data science techniques was demonstrated predicting optimising enantioselectivity regioselectivity catalytic reactions through optimisation ligands, counterions reaction conditions. Direct new catalysts/reactions is more difficult requires efficient exploration transition metal chemical space. A range computational descriptor generation, ranging from molecular mechanics to DFT methods, have successfully demonstrated, often conjunction with reduce cost associated TS calculations. Complex aspects reactions, such as solvent, temperature, etc., also incorporated into workflow.

Language: Английский

Citations

9
Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery DOI Creative Commons
Xiaobo Li, Yu Che, Linjiang Chen

et al.

Nature Chemistry, Journal Year: 2024, Volume and Issue: 16(8), P. 1286 - 1294

Published: June 11, 2024

Abstract Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities OPCs from first principles, either by expert knowledge or using priori calculations, as catalyst activity depends on complex interrelated properties. Organic photocatalysts and other systems have often been discovered mixture design trial error. Here we report two-step data-driven approach targeted synthesis subsequent reaction optimization for metallophotocatalysis, demonstrated decarboxylative sp 3 – 2 cross-coupling amino acids with aryl halides. Our uses Bayesian strategy coupled encoding key physical properties molecular descriptors identify promising virtual library 560 candidate molecules. This led OPC formulations that are competitive iridium exploring just 2.4% available formulation space (107 4,500 possible conditions).

Language: Английский

Citations

8
Catalyst-Controlled Enantioselective and Regiodivergent Addition of Aryl Boron Nucleophiles to N-Alkyl Nicotinate Salts DOI
Kacey G. Ortiz,

Jordan J. Dotson,

Donovan J. Robinson

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(21), P. 11781 - 11788

Published: May 19, 2023

Dihydropyridines are versatile building blocks for the synthesis of pyridines, tetrahydropyridines, and piperidines. Addition nucleophiles to activated pyridinium salts allows 1,2-, 1,4-, or 1,6-dihydropyridines; however, this process often leads a mixture constitutional isomers. Catalyst-controlled regioselective addition pyridiniums has potential solve problem. Herein, we report that boron-based can be accomplished by choice Rh catalyst.

Language: Английский

Citations

22
A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target DOI Creative Commons
Simone Gallarati, Puck van Gerwen, Rubén Laplaza

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(10), P. 3640 - 3660

Published: Jan. 1, 2024

A genetic optimization strategy to discover asymmetric organocatalysts with high activity and enantioselectivity across a broad substrate scope.

Language: Английский

Citations

7