Catalyzing Change: The Power of Computational Asymmetric Catalysis DOI Creative Commons
Sharon Pinus, Jérôme Genzling, Mihai Burai Patrascu

et al.

Published: Dec. 13, 2023

Computational asymmetric catalysis has seen an impressive rise in the last twenty years, thanks to advancements algorithm and method development for predicting catalyst enantioselectivity. These methods/algorithms describe reactions that can be categorized into two groups: where 1) knowledge of mechanism is not required leveraging experimental data establish correlations between reaction descriptors enantioselectivity imperative, 2) (or transition state (TS) enantioselective step) known used determine stereoselectivity by modeling diastereomeric TSs. Although these methods have reached important level proficiency prediction, this field remains largely obscured chemists. In review, we aim shed light on models, methods, applications synthesis, with accessible language suited Our hope will ultimately adopted synthetic chemists design novel catalysts.

Language: Английский

%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
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

15
Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts DOI Creative Commons
Adarsh V. Kalikadien, Cecile Valsecchi, Robbert van Putten

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(34), P. 13618 - 13630

Published: Jan. 1, 2024

Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and selecting a catalyst induces the desired reactivity or selectivity trivial task. Nonetheless, ligand engineering selection any new prochiral olefin remains an empirical trial-error exercise. In study, we investigated whether machine learning techniques could be used to accelerate identification most efficient ligand. For purpose, high throughput experimentation build large dataset consisting results Rh-catalyzed asymmetric hydrogenation, specially designed applications in learning. We showcased its alignment with existing literature while addressing observed discrepancies. Additionally, computational framework automated reproducible quantum-chemistry based featurization structures was created. Together less computationally demanding representations, these descriptors were fed into our pipeline both out-of-domain in-domain prediction tasks reactivity. purposes, models provided limited efficacy. It found even expensive do not impart significant meaning model predictions. The application, partly successful predictions conversion, emphasizes need evaluating cost-benefit ratio intensive tailored descriptor design. Challenges persist predicting enantioselectivity, calling caution interpreting from small datasets. Our insights underscore importance diversity broad substrate inclusion suggest mechanistic considerations improve accuracy statistical models.

Language: Английский

Citations

6
Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation DOI Creative Commons
Margareth S. Baidun, Adarsh V. Kalikadien, Laurent Lefort

et al.

The Journal of Physical Chemistry C, Journal Year: 2024, Volume and Issue: 128(19), P. 7987 - 7998

Published: May 2, 2024

Data-driven catalyst design is a promising approach for addressing the challenges in identifying suitable catalysts synthetic transformations. Models with descriptor calculations relying solely on precatalyst structure are potentially generalizable but may overlook catalyst–substrate interactions. This study explores substrate-specific interactions context of Rh-catalyzed asymmetric hydrogenation to elucidate impact substrate inclusion and descriptors derived from it. We compare complex methyl 2-acetamidoacrylate as model generic involving placeholder substrate, norbornadiene, across 11 Rh-based bidentate bisphosphine ligands. For these systems, full conformer ensemble analysis reveals an intriguing finding: rigid induces conformational freedom ligand. flexibility gives rise more diverse landscape, showing previously overlooked aspect dynamics. Electronic variations particularly highlight differences between structures. suggests that precatalyst-like models lack crucial insights into catalyst. speculate such be general phenomenon can influence development predictive computational TM-based catalysis.

Language: Английский

Citations

4
Tuning the steric hindrance of alkylamines: a predictive model of steric editing of planar amines DOI Creative Commons
Michele Tomasini, Maria Voccia, Lucia Caporaso

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: 15(33), P. 13405 - 13414

Published: Jan. 1, 2024

Amines are one of the most prevalent functional groups in chemistry.

Language: Английский

Citations

4
Recent advances of machine learning applications in the development of experimental homogeneous catalysis DOI Creative Commons

Nil Sanosa,

David Dalmau, Diego Sampedro

et al.

Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(1), P. 100068 - 100068

Published: April 27, 2024

Language: Английский

Citations

3
Understanding Catalytic Enantioselective C–H Bond Oxidation at Nonactivated Methylenes Through Predictive Statistical Modeling Analysis DOI Creative Commons
Arnau Call, Andrea Palone, Jordan P. Liles

et al.

ACS Catalysis, Journal Year: 2025, Volume and Issue: 15(3), P. 2110 - 2123

Published: Jan. 22, 2025

Enantioselective C(sp3)-H bond oxidation is a powerful strategy for installing functionality in rich molecules. Site- and enantioselective of strong C-H bonds monosubstituted cyclohexanes with hydrogen peroxide catalyzed by aminopyridine manganese catalysts combination alkanoic acids has been recently described. Mechanistic uncertainties nonobvious enantioselectivity trends challenge development the full potential this reaction as synthetic tool. Herein, we apply predictive statistical analysis to identify mechanistically informative correlations that provide valuable understanding will guide future optimization reactions.

Language: Английский

Citations

0
Computational Tools for the Prediction of Site- and Regioselectivity of Organic Reactions DOI Creative Commons
Lukas M. Sigmund,

Michele Assante,

Magnus J. Johansson

et al.

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

Published: Jan. 1, 2025

This article reviews computational tools for the prediction of regio- and site-selectivity organic reactions. It spans from quantum chemical procedures to deep learning models showcases application presented tools.

Language: Английский

Citations

0
Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes DOI Creative Commons

Sára Finta,

Adarsh V. Kalikadien, Evgeny A. Pidko

et al.

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Transition-metal complexes serve as highly enantioselective homogeneous catalysts for various transformations, making them valuable in the pharmaceutical industry. Data-driven prediction models can accelerate high-throughput catalyst design but require computer-readable representations that account conformational flexibility. This is typically achieved through high-level conformer searches, followed by DFT optimization of transition-metal complexes. However, selection remains reliant on human assumptions, with no cost-efficient and generalizable workflow available. To address this, we introduce an automated approach to correlate CREST(GFN2-xTB//GFN-FF)-generated ensembles their DFT-optimized counterparts systematic selection. We analyzed 24 precatalyst structures, performing CREST full optimization. Three filtering methods were evaluated: (i) geometric ligand descriptors, (ii) PCA-based selection, (iii) DBSCAN clustering using RMSD energy. The proposed validated Rh-based featuring bisphosphine ligands, which are widely employed hydrogenation reactions. assess general applicability, both its corresponding acrylate-bound complex analyzed. Our results confirm overestimates flexibility, energy-based ineffective. failed distinguish conformers energy, while RMSD-based improved lacked tunability. provided most effective approach, eliminating redundancies preserving key configurations. method remained robust across data sets computationally efficient without requiring molecular descriptor calculations. These findings highlight limitations advantages structure-based approaches While a practical solution, parameters remain system-dependent. For high-accuracy applications, refined energy calculations may be necessary; however, DBSCAN-based offers accessible strategy rapid involving

Language: Английский

Citations

0
Rhenium Alkyne Catalysis: Sterics Control the Reactivity DOI Creative Commons
Michele Tomasini, Martí Gimferrer, Lucia Caporaso

et al.

Inorganic Chemistry, Journal Year: 2024, Volume and Issue: 63(13), P. 5842 - 5851

Published: March 20, 2024

Metathesis reactions, including alkane, alkene, and alkyne metatheses, have their origins in the fundamental understanding of chemical reactions development specialized catalysts. These stand as transformative pillars organic chemistry, providing efficient rearrangement carbon-carbon bonds enabling synthetic access to diverse complex compounds. Their impact spans industries such petrochemicals, pharmaceuticals, materials science. In this work, we present a detailed mechanistic study Re(V) catalyzed metathesis through density functional theory calculations. Our findings are agreement with experimental evidence from Jia co-workers unveil critical factors governing catalyst performance. work not only enhances our but also contributes broader landscape catalytic processes, facilitating design more selective transformations synthesis.

Language: Английский

Citations

2