ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Chen, Shu-Sen; Meyer, Zack; Jensen, Brendan; Kraus, Alex; Lambert, Allison; Ess, Daniel H.

doi:10.1021/acs.jcim.3c01310

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Journal Article · 20 November 2023 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.3c01310· OSTI ID:2352286

Chen, Shu-Sen ^[1]; Meyer, Zack ^[2]; Jensen, Brendan ^[2]; Kraus, Alex ^[2]; Lambert, Allison ^[2]; ^[2]

Brigham Young University, Provo, UT (United States); Brigham Young University
Brigham Young University, Provo, UT (United States)

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. Furthermore, we also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

View Accepted Manuscript (DOE)

Research Organization:: Brigham Young University, Provo, UT (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0018329

OSTI ID:: 2352286

Journal Information:: Journal of Chemical Information and Modeling, Journal Name: Journal of Chemical Information and Modeling Journal Issue: 23 Vol. 63; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (45)

The Computational Road to Better Catalysts Jover, Jesús; Fey, Natalie Chemistry - An Asian Journal, Vol. 9, Issue 7 https://doi.org/10.1002/asia.201301696	journal	March 2014
Development of a Ligand Knowledge Base, Part 1: Computational Descriptors for Phosphorus Donor Ligands Fey, Natalie; Tsipis, Athanassios C.; Harris, Stephanie E. Chemistry - A European Journal, Vol. 12, Issue 1 https://doi.org/10.1002/chem.200500891	journal	January 2006
Computational Mapping of Dirhodium(II) Catalysts Green, Adam I.; Tinworth, Christopher P.; Warriner, Stuart Chemistry – A European Journal, Vol. 27, Issue 7 https://doi.org/10.1002/chem.202003801	journal	January 2021
molSimplify: A toolkit for automating discovery in inorganic chemistry Ioannidis, Efthymios I.; Gani, Terry Z. H.; Kulik, Heather J. Journal of Computational Chemistry, Vol. 37, Issue 22 https://doi.org/10.1002/jcc.24437	journal	July 2016
QChASM : Quantum chemistry automation and structure manipulation Ingman, Victoria M.; Schaefer, Anthony J.; Andreola, Laura R. WIREs Computational Molecular Science, Vol. 11, Issue 4 https://doi.org/10.1002/wcms.1510	journal	December 2020
The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals Zhao, Yan; Truhlar, Donald G. Theoretical Chemistry Accounts, Vol. 120, Issue 1-3 https://doi.org/10.1007/s00214-007-0310-x	journal	July 2007
Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States Chen, Shusen; Nielson, Taylor; Zalit, Elayna Topics in Catalysis, Vol. 65, Issue 1-4 https://doi.org/10.1007/s11244-021-01506-0	journal	September 2021
Building ligand knowledge bases for organometallic chemistry: Computational description of phosphorus(III)-donor ligands and the metal–phosphorus bond Fey, Natalie; Orpen, A. Guy; Harvey, Jeremy N. Coordination Chemistry Reviews, Vol. 253, Issue 5-6 https://doi.org/10.1016/j.ccr.2008.04.017	journal	March 2009
Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design Janet, Jon Paul; Duan, Chenru; Nandy, Aditya Accounts of Chemical Research, Vol. 54, Issue 3 https://doi.org/10.1021/acs.accounts.0c00686	journal	January 2021
Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis Durand, Derek J.; Fey, Natalie Accounts of Chemical Research, Vol. 54, Issue 4 https://doi.org/10.1021/acs.accounts.0c00807	journal	February 2021
Data Science Meets Physical Organic Chemistry Crawford, Jennifer M.; Kingston, Cian; Toste, F. Dean Accounts of Chemical Research, Vol. 54, Issue 16 https://doi.org/10.1021/acs.accounts.1c00285	journal	August 2021
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning Nandy, Aditya; Duan, Chenru; Taylor, Michael G. Chemical Reviews, Vol. 121, Issue 16 https://doi.org/10.1021/acs.chemrev.1c00347	journal	July 2021
Computational Ligand Descriptors for Catalyst Design Durand, Derek J.; Fey, Natalie Chemical Reviews, Vol. 119, Issue 11 https://doi.org/10.1021/acs.chemrev.8b00588	journal	October 2018
A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts Taylor, Robin; Wood, Peter A. Chemical Reviews, Vol. 119, Issue 16 https://doi.org/10.1021/acs.chemrev.9b00155	journal	June 2019
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry Janet, Jon Paul; Liu, Fang; Nandy, Aditya Inorganic Chemistry, Vol. 58, Issue 16 https://doi.org/10.1021/acs.inorgchem.9b00109	journal	March 2019
tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes Balcells, David; Skjelstad, Bastian Bjerkem Journal of Chemical Information and Modeling, Vol. 60, Issue 12 https://doi.org/10.1021/acs.jcim.0c01041	journal	November 2020
DENOPTIM: Software for Computational de Novo Design of Organic and Inorganic Molecules Foscato, Marco; Venkatraman, Vishwesh; Jensen, Vidar R. Journal of Chemical Information and Modeling, Vol. 59, Issue 10 https://doi.org/10.1021/acs.jcim.9b00516	journal	September 2019
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands Duan, Chenru; Ladera, Adriana J.; Liu, Julian C. -L. Journal of Chemical Theory and Computation, Vol. 18, Issue 8 https://doi.org/10.1021/acs.jctc.2c00468	journal	July 2022
AARON: An Automated Reaction Optimizer for New Catalysts Guan, Yanfei; Ingman, Victoria M.; Rooks, Benjamin J. Journal of Chemical Theory and Computation, Vol. 14, Issue 10 https://doi.org/10.1021/acs.jctc.8b00578	journal	August 2018
GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions Bannwarth, Christoph; Ehlert, Sebastian; Grimme, Stefan Journal of Chemical Theory and Computation, Vol. 15, Issue 3 https://doi.org/10.1021/acs.jctc.8b01176	journal	January 2019
Impact of Phosphine Featurization Methods in Process Development Crawford, Jennifer M.; Gensch, Tobias; Sigman, Matthew S. Organic Process Research & Development, Vol. 26, Issue 4 https://doi.org/10.1021/acs.oprd.1c00357	journal	March 2022
Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis Matsuoka, Wataru; Harabuchi, Yu; Maeda, Satoshi ACS Catalysis, Vol. 12, Issue 7 https://doi.org/10.1021/acscatal.2c00267	journal	March 2022
Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling Gensch, Tobias; Smith, Sleight R.; Colacot, Thomas J. ACS Catalysis, Vol. 12, Issue 13 https://doi.org/10.1021/acscatal.2c01970	journal	June 2022
Computational Transition-State Design Provides Experimentally Verified Cr(P,N) Catalysts for Control of Ethylene Trimerization and Tetramerization Kwon, Doo-Hyun; Fuller, Jack T.; Kilgore, Uriah J. ACS Catalysis, Vol. 8, Issue 2 https://doi.org/10.1021/acscatal.7b04026	journal	January 2018
Machine Learning for Catalysis Informatics: Recent Applications and Prospects Toyao, Takashi; Maeno, Zen; Takakusagi, Satoru ACS Catalysis, Vol. 10, Issue 3 https://doi.org/10.1021/acscatal.9b04186	journal	December 2019
Automated in Silico Design of Homogeneous Catalysts Foscato, Marco; Jensen, Vidar R. ACS Catalysis, Vol. 10, Issue 3 https://doi.org/10.1021/acscatal.9b04952	journal	January 2020
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization Janet, Jon Paul; Ramesh, Sahasrajit; Duan, Chenru ACS Central Science, Vol. 6, Issue 4 https://doi.org/10.1021/acscentsci.0c00026	journal	March 2020
The Evolution of Data-Driven Modeling in Organic Chemistry Williams, Wendy L.; Zeng, Lingyu; Gensch, Tobias ACS Central Science, Vol. 7, Issue 10 https://doi.org/10.1021/acscentsci.1c00535	journal	October 2021
The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. Morgan, H. L. Journal of Chemical Documentation, Vol. 5, Issue 2 https://doi.org/10.1021/c160017a018	journal	May 1965
Adding Value to Crystallographically-Derived Knowledge Bases ^† Fey, Natalie; Harris, Stephanie E.; Harvey, Jeremy N. Journal of Chemical Information and Modeling, Vol. 46, Issue 2 https://doi.org/10.1021/ci0504768	journal	March 2006
Automated Design of Realistic Organometallic Molecules from Fragments Foscato, Marco; Occhipinti, Giovanni; Venkatraman, Vishwesh Journal of Chemical Information and Modeling, Vol. 54, Issue 3 https://doi.org/10.1021/ci4007497	journal	February 2014
Statistical Modeling of a Ligand Knowledge Base Mansson, Ralph A.; Welsh, Alan H.; Fey, Natalie Journal of Chemical Information and Modeling, Vol. 46, Issue 6 https://doi.org/10.1021/ci600212t	journal	November 2006
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis Gensch, Tobias; dos Passos Gomes, Gabriel; Friederich, Pascal Journal of the American Chemical Society, Vol. 144, Issue 3 https://doi.org/10.1021/jacs.1c09718	journal	January 2022
Expansion of the Ligand Knowledge Base for Monodentate P-Donor Ligands (LKB-P) ^† Jover, Jesús; Fey, Natalie; Harvey, Jeremy N. Organometallics, Vol. 29, Issue 23 https://doi.org/10.1021/om100648v	journal	December 2010
Computational Descriptors for Chelating P,P- and P,N-Donor Ligands ¹ Fey, Natalie; Harvey, Jeremy N.; Lloyd-Jones, Guy C. Organometallics, Vol. 27, Issue 7 https://doi.org/10.1021/om700840h	journal	April 2008
cell2mol: encoding chemistry to interpret crystallographic data Vela, Sergi; Laplaza, Ruben; Cho, Yuri npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00874-9	journal	August 2022
Towards the online computer-aided design of catalytic pockets Falivene, Laura; Cao, Zhen; Petta, Andrea Nature Chemistry, Vol. 11, Issue 10 https://doi.org/10.1038/s41557-019-0319-5	journal	September 2019
Mapping the properties of bidentate ligands with calculated descriptors (LKB-bid) Fey, Natalie; Koumi, Alexander; Malkov, Andrei V. Dalton Transactions, Vol. 49, Issue 24 https://doi.org/10.1039/D0DT01694B	journal	January 2020
Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex Friederich, Pascal; dos Passos Gomes, Gabriel; De Bin, Riccardo Chemical Science, Vol. 11, Issue 18 https://doi.org/10.1039/D0SC00445F	journal	January 2020
ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold Kalikadien, Adarsh V.; Pidko, Evgeny A.; Sinha, Vivek Digital Discovery, Vol. 1, Issue 1 https://doi.org/10.1039/D1DD00017A	journal	January 2022
Deep learning metal complex properties with natural quantum graphs Kneiding, Hannes; Lukin, Ruslan; Lang, Lucas Digital Discovery, Vol. 2, Issue 3 https://doi.org/10.1039/D2DD00129B	journal	January 2023
A molecular mechanics approach to mapping the conformational space of diaryl and triarylphosphines Fey, Natalie; Howell, James A. S.; Lovatt, Jonathan D. Dalton Transactions, Issue 46 https://doi.org/10.1039/b610123b	journal	January 2006
Rational design of diphosphorus ligands – a route to superior catalysts Gillespie, Jason A.; Dodds, Deborah L.; Kamer, Paul C. J. Dalton Transactions, Vol. 39, Issue 11 https://doi.org/10.1039/b913778e	journal	January 2010
Ligand additivity relationships enable efficient exploration of transition metal chemical space Arunachalam, Naveen; Gugler, Stefan; Taylor, Michael G. The Journal of Chemical Physics, Vol. 157, Issue 18 https://doi.org/10.1063/5.0125700	journal	November 2022
Mordred: a molecular descriptor calculator Moriwaki, Hirotomo; Tian, Yu-Shi; Kawashita, Norihito Journal of Cheminformatics, Vol. 10, Issue 1 https://doi.org/10.1186/s13321-018-0258-y	journal	February 2018

Similar Records

First-row transition metal complexes of ENENES ligands: the ability of the thioether donor to impact the coordination chemistry

Journal Article · 2015 · Dalton Transactions · OSTI ID:1457264

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Catalysts
Chemical structure
Ligands
Metals
Transition metals

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Citation Formats

References (45)

Similar Records

Related Subjects