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Cho Y, Laplaza R, Vela S, Corminboeuf C. Automated prediction of ground state spin for transition metal complexes. DIGITAL DISCOVERY 2024; 3:1638-1647. [PMID: 39118977 PMCID: PMC11305380 DOI: 10.1039/d4dd00093e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024]
Abstract
Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes.
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Affiliation(s)
- Yuri Cho
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- National Centre for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Sergi Vela
- Departament de Ciència de Materials i Química Física and IQTCUB, Universitat de Barcelona Barcelona Spain
- Institut de Química Avançada de Catalunya (IQAC-CSIC) Barcelona Spain
| | - Clémence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- National Centre for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne Lausanne Switzerland
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2
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Gallarati S, van Gerwen P, Laplaza R, Brey L, Makaveev A, Corminboeuf C. A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target. Chem Sci 2024; 15:3640-3660. [PMID: 38455002 PMCID: PMC10915838 DOI: 10.1039/d3sc06208b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 03/09/2024] Open
Abstract
A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as a primary target. We apply this strategy to the Pictet-Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure-performance relationships from the challenging areas of chemical space.
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Affiliation(s)
- Simone Gallarati
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Puck van Gerwen
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Ruben Laplaza
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Lucien Brey
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Alexander Makaveev
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
| | - Clemence Corminboeuf
- Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Competence in Research - Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland
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3
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Célerse F, Wodrich MD, Vela S, Gallarati S, Fabregat R, Juraskova V, Corminboeuf C. From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials. J Chem Inf Model 2024; 64:1201-1212. [PMID: 38319296 PMCID: PMC10900300 DOI: 10.1021/acs.jcim.3c01953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/07/2024]
Abstract
Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.
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Affiliation(s)
- Frédéric Célerse
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Matthew D. Wodrich
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Sergi Vela
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Simone Gallarati
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Raimon Fabregat
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Veronika Juraskova
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Clémence Corminboeuf
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
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Mayo Yanes E, Chakraborty S, Gershoni-Poranne R. COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems. Sci Data 2024; 11:97. [PMID: 38242917 PMCID: PMC10799083 DOI: 10.1038/s41597-024-02927-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/05/2024] [Indexed: 01/21/2024] Open
Abstract
Polycyclic aromatic systems are highly important to numerous applications, in particular to organic electronics and optoelectronics. High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available dataset of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date. Our dataset contains ~500k molecules comprising 11 types of aromatic and antiaromatic building blocks calculated at the GFN1-xTB level and is representative of a highly diverse chemical space. We detail the structure enumeration process and the methods used to provide various electronic properties (including HOMO-LUMO gap, adiabatic ionization potential, and adiabatic electron affinity). Additionally, we benchmark against a ~50k dataset calculated at the CAM-B3LYP-D3BJ/def2-SVP level and develop a fitting scheme to correct the xTB values to higher accuracy. These new datasets represent the second installment in the COMputational database of Polycyclic Aromatic Systems (COMPAS) Project.
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Affiliation(s)
- Eduardo Mayo Yanes
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Sabyasachi Chakraborty
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Renana Gershoni-Poranne
- Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa, 32000, Israel.
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5
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Raghavan P, Haas BC, Ruos ME, Schleinitz J, Doyle AG, Reisman SE, Sigman MS, Coley CW. Dataset Design for Building Models of Chemical Reactivity. ACS CENTRAL SCIENCE 2023; 9:2196-2204. [PMID: 38161380 PMCID: PMC10755851 DOI: 10.1021/acscentsci.3c01163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/06/2023] [Accepted: 11/15/2023] [Indexed: 01/03/2024]
Abstract
Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.
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Affiliation(s)
- Priyanka Raghavan
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Brittany C. Haas
- Department
of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Madeline E. Ruos
- Department
of Chemistry & Biochemistry, University
of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jules Schleinitz
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Abigail G. Doyle
- Department
of Chemistry & Biochemistry, University
of California, Los Angeles, Los Angeles, California 90095, United States
| | - Sarah E. Reisman
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Matthew S. Sigman
- Department
of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Connor W. Coley
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
- Department
of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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6
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Kevlishvili I, Duan C, Kulik HJ. Classification of Hemilabile Ligands Using Machine Learning. J Phys Chem Lett 2023:11100-11109. [PMID: 38051982 DOI: 10.1021/acs.jpclett.3c02828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Hemilabile ligands have the capacity to partially disengage from a metal center, providing a strategy to balance stability and reactivity in catalysis, but they are not straightforward to identify. We identify ligands in the Cambridge Structural Database that have been crystallized with distinct denticities and are thus identifiable as hemilabile ligands. We implement a semi-supervised learning approach using a label-spreading algorithm to augment a small negative set that is supported by heuristic rules of ligand and metal co-occurrence. We show that a heuristic based on coordinating atom identity alone is not sufficient to identify whether a ligand is hemilabile, and our trained machine-learning classification models are instead needed to predict whether a bi-, tri-, or tetradentate ligand is hemilabile with high accuracy and precision. Feature importance analysis of our models shows that the second, third, and fourth coordination spheres all play important roles in ligand hemilability.
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Affiliation(s)
- Ilia Kevlishvili
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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7
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Casetti N, Alfonso-Ramos JE, Coley CW, Stuyver T. Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery. Chemistry 2023; 29:e202301957. [PMID: 37526059 DOI: 10.1002/chem.202301957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field.
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Affiliation(s)
- Nicholas Casetti
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, United States
| | - Javier E Alfonso-Ramos
- Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75005, Paris, France
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, United States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, United States
| | - Thijs Stuyver
- Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75005, Paris, France
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8
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Xie X, Troisi A. Identification via Virtual Screening of Emissive Molecules with a Small Exciton-Vibration Coupling for High Color Purity and Potential Large Exciton Delocalization. J Phys Chem Lett 2023; 14:4119-4126. [PMID: 37129191 PMCID: PMC10165648 DOI: 10.1021/acs.jpclett.3c00749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A sequence of quantum chemical computations of increasing accuracy was used in this work to identify molecules with small exciton reorganization energy (exciton-vibration coupling), of interest for light emitting devices and coherent exciton transport, starting from a set of ∼4500 known molecules. We validated an approximate computational approach based on single-point calculations of the force in the excited state, which was shown to be very efficient in identifying the most promising candidates. We showed that a simple descriptor based on the bond order could be used to find molecules with potentially small exciton reorganization energies without performing excited state calculations. A small set of chemically diverse molecules with a small exciton reorganization energy was analyzed in greater detail to identify common features leading to this property. Many such molecules display an A-B-A structure where the bonding/antibonding patterns in the fragments A are similar in HOMO and LUMO. Another group of molecules with small reorganization energy displays instead HOMO and LUMO with a strong nonbonding character.
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Affiliation(s)
- Xiaoyu Xie
- Department of Chemistry, University of Liverpool Liverpool L69 3BX, U.K
| | - Alessandro Troisi
- Department of Chemistry, University of Liverpool Liverpool L69 3BX, U.K
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