An Unsupervised Machine Learning Workflow for Assigning and Predicting Generality in Asymmetric Catalysis DOI Creative Commons
Isaiah O. Betinol,

Saumya Thakur,

Jolene P. Reid

et al.

Published: Dec. 16, 2022

The development of chiral catalysts that can provide high enantioselectivities across a wide assortment substrates or reaction range is priority for many catalyst design efforts. While several approaches are available to aid in the identification general systems there currently no simple procedure directly measuring how given could be. Herein, we present catalyst-agnostic workflow centered on unsupervised machine learning enables rapid assessment and quantification generality. uses curated literature data sets descriptors visualize cluster chemical space coverage. This network then be applied derive generality metric through designer equations interfaced with other regression techniques prediction. As validating case studies, have successfully this method identify-through-quantification most chemotype an organocatalytic asymmetric Mannich predicted phosphoric acid addition nucleophile imines. mechanistic basis gleaned from calculated values by deconstructing contributions enantiomeric excess overall result. We conclude broadly applicable may more adaptative changes reactant structure because enantioinduction does not rely single set noncovalent interactions. In contrast, some work engaging robust contacts do change significantly nature when component altered. Ultimately, our findings represent framework interrogating predicting generality, strategy should relevant catalytic widely synthesis.

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

77
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

37
A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis DOI
Isaiah O. Betinol, Junshan Lai,

Saumya Thakur

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(23), P. 12870 - 12883

Published: June 2, 2023

The development of chiral catalysts that can provide high enantioselectivities across a wide assortment substrates or reaction range is priority for many catalyst design efforts. While several approaches are available to aid in the identification general systems, there currently no simple procedure directly measuring how given could be. Herein, we present catalyst-agnostic workflow centered on unsupervised machine learning enables rapid assessment and quantification generality. uses curated literature data sets descriptors visualize cluster chemical space coverage. This network then be applied derive generality metric through designer equations interfaced with other regression techniques prediction. As validating case studies, have successfully this method identify-through-quantification most chemotype an organocatalytic asymmetric Mannich predicted phosphoric acid addition nucleophiles imines. mechanistic basis gleaned from calculated values by deconstructing contributions enantiomeric excess overall result. Finally, our permitted mechanistically informative screening allow experimentalists rationally select highest probability achieving good result first round development. Overall, findings represent framework interrogating generality, strategy should relevant catalytic systems widely synthesis.

Language: Английский

Citations

31
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

11
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
COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems DOI Creative Commons
Eduardo Mayo Yanes, Sabyasachi Chakraborty, Renana Gershoni‐Poranne

et al.

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 19, 2024

Polycyclic aromatic systems are highly important to numerous applications, in particular organic electronics and optoelectronics. High-throughput screening generative models that can help identify new molecules advance these technologies require large amounts of high-quality data, which is expensive generate. In this report, we present the largest freely available dataset geometries properties cata-condensed poly(hetero)cyclic calculated date. Our contains ~500k comprising 11 types antiaromatic building blocks at GFN1-xTB level representative a diverse chemical space. We detail structure enumeration process methods used provide various electronic (including HOMO-LUMO gap, adiabatic ionization potential, electron affinity). Additionally, benchmark against ~50k CAM-B3LYP-D3BJ/def2-SVP develop fitting scheme correct xTB values higher accuracy. These datasets represent second installment COMputational database Aromatic Systems (COMPAS) Project.

Language: Английский

Citations

5
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
Overview on Building Blocks and Applications of Efficient and Robust Extended Tight Binding DOI
Abylay Katbashev, Marcel Stahn, Thomas Rose

et al.

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

Published: Feb. 27, 2025

The extended tight binding (xTB) family of methods opened many new possibilities in the field computational chemistry. Within just 5 years, GFN2-xTB parametrization for all elements up to Z = 86 enabled more than a thousand applications, which were previously not feasible with other electronic structure methods. xTB provide robust and efficient way apply quantum mechanics-based approaches obtaining molecular geometries, computing free energy corrections or describing noncovalent interactions found applicability targets. A crucial contribution success is availability within simulation packages frameworks, supported by open source development its program library packages. We present comprehensive summary applications capabilities different fields Moreover, we consider main software calculations, covering their current ecosystem, novel features, usage scientific community.

Language: Английский

Citations

0
Clc-db: an open-source online database of chiral ligands and catalysts DOI Creative Commons
Gufeng Yu, Kaiwen Yu, Xi Wang

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 3, 2025

Language: Английский

Citations

0
Automated prediction of ground state spin for transition metal complexes DOI Creative Commons
Yuri Cho, Rubén Laplaza, Sergi Vela

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(8), P. 1638 - 1647

Published: Jan. 1, 2024

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of molecular structure, charge spin from information file. Here, we develop a general approach to assign ground state transition metal complexes, in complement our previous efforts on determining oxidation states bond order within

Language: Английский

Citations

3