1
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Adhikari K, Vanermen M, Da Silva G, Van den Wyngaert T, Augustyns K, Elvas F. Trans-cyclooctene-a Swiss army knife for bioorthogonal chemistry: exploring the synthesis, reactivity, and applications in biomedical breakthroughs. EJNMMI Radiopharm Chem 2024; 9:47. [PMID: 38844698 PMCID: PMC11156836 DOI: 10.1186/s41181-024-00275-x] [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: 03/25/2024] [Accepted: 05/27/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Trans-cyclooctenes (TCOs) are highly strained alkenes with remarkable reactivity towards tetrazines (Tzs) in inverse electron-demand Diels-Alder reactions. Since their discovery as bioorthogonal reaction partners, novel TCO derivatives have been developed to improve their reactivity, stability, and hydrophilicity, thus expanding their utility in diverse applications. MAIN BODY TCOs have garnered significant interest for their applications in biomedical settings. In chemical biology, TCOs serve as tools for bioconjugation, enabling the precise labeling and manipulation of biomolecules. Moreover, their role in nuclear medicine is substantial, with TCOs employed in the radiolabeling of peptides and other biomolecules. This has led to their utilization in pretargeted nuclear imaging and therapy, where they function as both bioorthogonal tags and radiotracers, facilitating targeted disease diagnosis and treatment. Beyond these applications, TCOs have been used in targeted cancer therapy through a "click-to-release" approach, in which they act as key components to selectively deliver therapeutic agents to cancer cells, thereby enhancing treatment efficacy while minimizing off-target effects. However, the search for a suitable TCO scaffold with an appropriate balance between stability and reactivity remains a challenge. CONCLUSIONS This review paper provides a comprehensive overview of the current state of knowledge regarding the synthesis of TCOs, and its challenges, and their development throughout the years. We describe their wide ranging applications as radiolabeled prosthetic groups for radiolabeling, as bioorthogonal tags for pretargeted imaging and therapy, and targeted drug delivery, with the aim of showcasing the versatility and potential of TCOs as valuable tools in advancing biomedical research and applications.
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Affiliation(s)
- Karuna Adhikari
- Laboratory of Medicinal Chemistry, University of Antwerp, Antwerp, Belgium
- Molecular Imaging and Radiology, University of Antwerp, Antwerp, Belgium
| | - Maarten Vanermen
- Laboratory of Medicinal Chemistry, University of Antwerp, Antwerp, Belgium
- Molecular Imaging and Radiology, University of Antwerp, Antwerp, Belgium
| | - Gustavo Da Silva
- Molecular Imaging and Radiology, University of Antwerp, Antwerp, Belgium
| | - Tim Van den Wyngaert
- Molecular Imaging and Radiology, University of Antwerp, Antwerp, Belgium
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Koen Augustyns
- Laboratory of Medicinal Chemistry, University of Antwerp, Antwerp, Belgium.
| | - Filipe Elvas
- Molecular Imaging and Radiology, University of Antwerp, Antwerp, Belgium.
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium.
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2
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Svatunek D. Computational Organic Chemistry: The Frontier for Understanding and Designing Bioorthogonal Cycloadditions. Top Curr Chem (Cham) 2024; 382:17. [PMID: 38727989 PMCID: PMC11087259 DOI: 10.1007/s41061-024-00461-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 04/06/2024] [Indexed: 05/13/2024]
Abstract
Computational organic chemistry has become a valuable tool in the field of bioorthogonal chemistry, offering insights and aiding in the progression of this branch of chemistry. In this review, I present an overview of computational work in this field, including an exploration of both the primary computational analysis methods used and their application in the main areas of bioorthogonal chemistry: (3 + 2) and [4 + 2] cycloadditions. In the context of (3 + 2) cycloadditions, detailed studies of electronic effects have informed the evolution of cycloalkyne/1,3-dipole cycloadditions. Through computational techniques, researchers have found ways to adjust the electronic structure via hyperconjugation to enhance reactions without compromising stability. For [4 + 2] cycloadditions, methods such as distortion/interaction analysis and energy decomposition analysis have been beneficial, leading to the development of bioorthogonal reactants with improved reactivity and the creation of orthogonal reaction pairs. To conclude, I touch upon the emerging fields of cheminformatics and machine learning, which promise to play a role in future reaction discovery and optimization.
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Affiliation(s)
- Dennis Svatunek
- Institute of Applied Synthetic Chemistry, Technische Universität Wien (TU Wien), Getreidemarkt 9, 1060, Vienna, Austria.
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3
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Schlein E, Rokka J, Odell LR, van den Broek SL, Herth MM, Battisti UM, Syvänen S, Sehlin D, Eriksson J. Synthesis and evaluation of fluorine-18 labelled tetrazines as pre-targeting imaging agents for PET. EJNMMI Radiopharm Chem 2024; 9:21. [PMID: 38446356 PMCID: PMC10917718 DOI: 10.1186/s41181-024-00250-6] [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: 02/04/2024] [Accepted: 02/28/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND The brain is a challenging target for antibody-based positron emission tomography (immunoPET) imaging due to the restricted access of antibody-based ligands through the blood-brain barrier (BBB). To overcome this physiological obstacle, we have previously developed bispecific antibody ligands that pass through the BBB via receptor-mediated transcytosis. While these radiolabelled ligands have high affinity and specificity, their long residence time in the blood and brain, typical for large molecules, poses another challenge for PET imaging. A viable solution could be a two-step pre-targeting approach which involves the administration of a tagged antibody that accumulates at the target site in the brain and then clears from the blood, followed by administration of a small radiolabelled molecule with fast kinetics. This radiolabelled molecule can couple to the tagged antibody and thereby make the antibody localisation visible by PET imaging. The in vivo linkage can be achieved by using the inverse electron demand Diels-Alder reaction (IEDDA), with trans-cyclooctene (TCO) and tetrazine groups participating as reactants. In this study, two novel 18F-labelled tetrazines were synthesized and evaluated for their potential use as pre-targeting imaging agents, i.e., for their ability to rapidly enter the brain and, if unbound, to be efficiently cleared with minimal background retention. RESULTS The two compounds, a methyl tetrazine [18F]MeTz and an H-tetrazine [18F]HTz were radiolabelled using a two-step procedure via [18F]F-Py-TFP synthesized on solid support followed by amidation with amine-bearing tetrazines, resulting in radiochemical yields of 24% and 22%, respectively, and a radiochemical purity of > 96%. In vivo PET imaging was performed to assess their suitability for in vivo pre-targeting. Time-activity curves from PET-scans showed [18F]MeTz to be the more pharmacokinetically suitable agent, given its fast and homogenous distribution in the brain and rapid clearance. However, in terms of rection kinetics, H-tetrazines are advantageous, exhibiting faster reaction rates in IEDDA reactions with dienophiles like trans-cyclooctenes, making [18F]HTz potentially more beneficial for pre-targeting applications. CONCLUSION This study demonstrates a significant potential of [18F]MeTz and [18F]HTz as agents for pre-targeted PET brain imaging due to their efficient brain uptake, swift clearance and appropriate chemical stability.
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Affiliation(s)
- Eva Schlein
- Department of Public Health and Caring Sciences, Uppsala University, 751 85, Uppsala, Sweden
| | - Johanna Rokka
- Department of Public Health and Caring Sciences, Uppsala University, 751 85, Uppsala, Sweden
| | - Luke R Odell
- Department of Medicinal Chemistry, Uppsala University, 751 23, Uppsala, Sweden
| | | | - Matthias M Herth
- Department of Drug Design and Pharmacology, University of Copenhagen, 2100, Copenhagen, Denmark
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Umberto M Battisti
- Department of Drug Design and Pharmacology, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Stina Syvänen
- Department of Public Health and Caring Sciences, Uppsala University, 751 85, Uppsala, Sweden
| | - Dag Sehlin
- Department of Public Health and Caring Sciences, Uppsala University, 751 85, Uppsala, Sweden
| | - Jonas Eriksson
- Department of Medicinal Chemistry, Uppsala University, 751 23, Uppsala, Sweden.
- PET Centre, Uppsala University Hospital, 751 85, Uppsala, Sweden.
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4
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Adrion DM, Karunaratne WV, Lopez SA. Multiconfigurational photodynamics simulations reveal the mechanism of photodecarbonylations of cyclopropenones in explicit aqueous environments. Chem Sci 2023; 14:13205-13218. [PMID: 38023495 PMCID: PMC10664470 DOI: 10.1039/d3sc03805j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023] Open
Abstract
Gas-evolving photochemical reactions use light and mild conditions to access strained organic compounds irreversibly. Cyclopropenones are a class of light-responsive molecules used in bioorthogonal photoclick reactions; their excited-state decarbonylation reaction mechanisms are misunderstood due to their ultrafast (<100 femtosecond) lifetimes. We have combined multiconfigurational quantum mechanical (QM) calculations and non-adiabatic molecular dynamics (NAMD) simulations to uncover the excited-state mechanism of cyclopropenone and a photoprotected cyclooctyne-(COT)-precursor in gaseous and explicit aqueous environments. We explore the role of H-bonding with fully quantum mechanical explicitly solvated NAMD simulations for the decarbonylation reaction. The cyclopropenones pass through asynchronous conical intersections and have dynamically concerted photodecarbonylation mechanisms. The COT-precursor has a higher quantum yield of 55% than cyclopropenone (28%) because these trajectories prefer to break a σCC bond to avoid the strained trans-cyclooctene geometries. Our solvated simulations show an increased quantum yield (58%) for the systems studied here.
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Affiliation(s)
- Daniel M Adrion
- Department of Chemistry and Chemical Biology, Northeastern University Boston Massachusetts 02115 USA
| | - Waruni V Karunaratne
- Department of Chemistry and Chemical Biology, Northeastern University Boston Massachusetts 02115 USA
| | - Steven A Lopez
- Department of Chemistry and Chemical Biology, Northeastern University Boston Massachusetts 02115 USA
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5
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Li SW, Xu LC, Zhang C, Zhang SQ, Hong X. Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge. Nat Commun 2023; 14:3569. [PMID: 37322041 DOI: 10.1038/s41467-023-39283-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023] Open
Abstract
Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.
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Affiliation(s)
- Shu-Wen Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China
| | - Li-Cheng Xu
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China
| | - Cheng Zhang
- Department of Chemistry, University of Science and Technology of China, Hefei, China
| | - Shuo-Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, 310027, China.
- Beijing National Laboratory for Molecular Sciences, Zhongguancun North First Street No. 2, Beijing, 100190, PR China.
- Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province, School of Science, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang Province, China.
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6
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Gokulu IS, Banta S. Biotechnology applications of proteins functionalized with DNA oligonucleotides. Trends Biotechnol 2023; 41:575-585. [PMID: 36115723 DOI: 10.1016/j.tibtech.2022.08.004] [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: 07/11/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 10/14/2022]
Abstract
The functionalization of proteins with DNA through the formation of covalent bonds enables a wide range of biotechnology advancements. For example, single-molecule analytical methods rely on bioconjugated DNA as elastic biolinkers for protein immobilization. Labeling proteins with DNA enables facile protein identification, as well as spatial and temporal organization and control of protein within DNA-protein networks. Bioconjugation reactions can target native, engineered, and non-canonical amino acids (NCAAs) within proteins. In addition, further protein engineering via the incorporation of peptide tags and self-labeling proteins can also be used for conjugation reactions. The selection of techniques will depend on application requirements such as yield, selectivity, conjugation position, potential for steric hindrance, cost, commercial availability, and potential impact on protein function.
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Affiliation(s)
- Ipek Simay Gokulu
- Department of Chemical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA
| | - Scott Banta
- Department of Chemical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
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7
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García-Andrade X, García Tahoces P, Pérez-Ríos J, Martínez Núñez E. Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations. J Phys Chem A 2023; 127:2274-2283. [PMID: 36877614 PMCID: PMC10845151 DOI: 10.1021/acs.jpca.2c08340] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/19/2023] [Indexed: 03/07/2023]
Abstract
Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.
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Affiliation(s)
| | - Pablo García Tahoces
- Department
of Electronics and Computer Science, University
of Santiago de Compostela, Santiago de Compostela 15782, Spain
| | - Jesús Pérez-Ríos
- Department
of Physics, Stony Brook University, Stony Brook, New York 11794, United States
- Institute
for Advanced Computational Science, Stony
Brook University, Stony
Brook, New York 11794-3800, United States
| | - Emilio Martínez Núñez
- Department
of Physical Chemistry, University of Santiago
de Compostela, Santiago
de Compostela 15782, Spain
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8
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Beutick SE, Vermeeren P, Hamlin TA. The 1,3-Dipolar Cycloaddition: From Conception to Quantum Chemical Design. Chem Asian J 2022; 17:e202200553. [PMID: 35822651 PMCID: PMC9539489 DOI: 10.1002/asia.202200553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/08/2022] [Indexed: 11/12/2022]
Abstract
The 1,3-dipolar cycloaddition (1,3-DCA) reaction, conceptualized by Rolf Huisgen in 1960, has proven immensely useful in organic, material, and biological chemistry. The uncatalyzed, thermal transformation is generally sluggish and unselective, but the reactivity can be enhanced by means of metal catalysis or by the introduction of either predistortion or electronic tuning of the dipolarophile. These promoted reactions generally go with a much higher reactivity, selectivity, and yields, often at ambient temperatures. The rapid orthogonal reactivity and compatibility with aqueous and physiological conditions positions the 1,3-DCA as an excellent bioorthogonal reaction. Quantum chemical calculations have been critical for providing an understanding of the physical factors that control the reactivity and selectivity of 1,3-DCAs. In silico derived design principles have proven invaluable for the design of new dipolarophiles with tailored reactivity. This review discusses everything from the conception of the 1,3-DCA all the way to the state-of-the-art methods and models used for the quantum chemical design of novel (bioorthogonal) reagents.
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Affiliation(s)
- Steven E. Beutick
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081 HVAmsterdamThe Netherlands
| | - Pascal Vermeeren
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081 HVAmsterdamThe Netherlands
| | - Trevor A. Hamlin
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081 HVAmsterdamThe Netherlands
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9
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García-Aznar P, Escorihuela J. Computational insights into the inverse electron-demand Diels-Alder reaction of norbornenes with 1,2,4,5-tetrazines: norbornene substituents' effects on the reaction rate. Org Biomol Chem 2022; 20:6400-6412. [PMID: 35876298 DOI: 10.1039/d2ob01121b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The study of the reaction rates and mechanism of click chemistry reactions still remains an interesting challenge in organic chemistry. In this regard, the inverse electron demand Diels-Alder (IEDDA) reaction represents a promising metal-free alternative with enhanced reaction rates compared to other reactions of the click chemistry toolbox. Among the different types of dienophiles used in the IEDDA reactions, norbornenes have been widely used given their high stability and fast reaction rates. The inverse electron-demand Diels Alder reaction of 3,6-dipyridin-2-yl-1,2,4,5-tetrazine with a series of norbornene derivatives was studied with quantum mechanical calculations at the M06-2X/6-311+G(d,p) level of theory. The theoretical predictions were confirmed with the experimental data and analyzed with the use of the distortion/interaction model. The obtained results will help in obtaining a better understanding of the factors that affect the relative cycloaddition rates of norbornenes with tetrazines, which are crucial for selectively tuning their efficacy.
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Affiliation(s)
- Pablo García-Aznar
- Departamento de Química Orgánica, Facultad de Farmacia, Universitat de València, Avda. Vicente Andrés Estellés, s/n, Burjassot 46100, València, Spain.
| | - Jorge Escorihuela
- Departamento de Química Orgánica, Facultad de Farmacia, Universitat de València, Avda. Vicente Andrés Estellés, s/n, Burjassot 46100, València, Spain.
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10
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Lustosa DM, Milo A. Mechanistic Inference from Statistical Models at Different Data-Size Regimes. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Danilo M. Lustosa
- Department of Chemistry, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
| | - Anat Milo
- Department of Chemistry, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
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11
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Recent Advances in the Development of Tetrazine Ligation Tools for Pretargeted Nuclear Imaging. Pharmaceuticals (Basel) 2022; 15:ph15060685. [PMID: 35745604 PMCID: PMC9227058 DOI: 10.3390/ph15060685] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 01/25/2023] Open
Abstract
Tetrazine ligation has gained interest as a bio-orthogonal chemistry tool within the last decade. In nuclear medicine, tetrazine ligation is currently being explored for pretargeted approaches, which have the potential to revolutionize state-of-the-art theranostic strategies. Pretargeting has been shown to increase target-to-background ratios for radiopharmaceuticals based on nanomedicines, especially within early timeframes. This allows the use of radionuclides with short half-lives which are more suited for clinical applications. Pretargeting bears the potential to increase the therapeutic dose delivered to the target as well as reduce the respective dose to healthy tissue. Combined with the possibility to be applied for diagnostic imaging, pretargeting could be optimal for theranostic approaches. In this review, we highlight efforts that have been made to radiolabel tetrazines with an emphasis on imaging.
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12
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Ros E, Bellido M, Matarin JA, Gallen A, Martínez M, Rodríguez L, Verdaguer X, Ribas de Pouplana L, Riera A. Amino acids with fluorescent tetrazine ethers as bioorthogonal handles for peptide modification. RSC Adv 2022; 12:14321-14327. [PMID: 35702248 PMCID: PMC9096626 DOI: 10.1039/d2ra02531k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/05/2022] [Indexed: 11/21/2022] Open
Abstract
A set of 3-bromo-1,2,4,5-tetrazines with three distinct substitutions have been used as reagents for late-stage functionalization of small molecules through nucleophilic aromatic substitution. Spectroscopic studies of the products obtained proved that tetrazine ethers are intrinsically fluorescent. This fluorescence is lost upon inverse Electron-Demand Diels–Alder (iEDDA) cycloaddition with strained alkenes. Tetrazine-phenol ethers are rather interesting because they can undergo rapid iEDDA reactions with a second order rate constant (k2) compatible with bioorthogonal ligations. As a showcase, l-tyrosine was derivatized with 3-bromo-6-methyl-1,2,4,5-tetrazine and coupled to the peptide drug octreotide. This peptide was detected in cellular flow cytometry, and its fluorescence turned off through a bioorthogonal iEDDA cycloaddition with a strained alkene, showing for the first time the detection and reactivity of intrinsically fluorescent tetrazines in a biologically relevant context. The synthesis and characterization of fluorescent tetrazine ethers with bioorthogonal applicability pave the way for the generation of useful compounds for both detection and bioconjugation in vivo. Octreotide derivatized with the fluorogenic amino acid 6-methyltetratrazinyl tryosine. Emission spectra before and after the iEDDA cycloaddition.![]()
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Affiliation(s)
- Enric Ros
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
| | - Marina Bellido
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain
| | - Joan A Matarin
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain .,BCN Peptides S.A., Pol. Ind. Els Vinyets-Els Fogars Sector II, Ctra. Comarcal 244, Km. 22, 08777 Sant Quintí de Mediona Barcelona Spain
| | - Albert Gallen
- Departament de Química Inorgànica i Orgànica, Secció Química Inorgànica. Universitat de Barcelona Martí i Franquès 1 08028 Barcelona Spain
| | - Manuel Martínez
- Departament de Química Inorgànica i Orgànica, Secció Química Inorgànica. Universitat de Barcelona Martí i Franquès 1 08028 Barcelona Spain.,Institut de Nanociència i Nanotecnologia (IN2UB), Universitat de Barcelona 08028. Barcelona Spain
| | - Laura Rodríguez
- Departament de Química Inorgànica i Orgànica, Secció Química Inorgànica. Universitat de Barcelona Martí i Franquès 1 08028 Barcelona Spain.,Institut de Nanociència i Nanotecnologia (IN2UB), Universitat de Barcelona 08028. Barcelona Spain
| | - Xavier Verdaguer
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain .,Departament de Química Inorgànica i Orgànica, Secció Química Orgànica. Universitat de Barcelona Martí i Franquès 1 08028 Barcelona Spain
| | - Lluís Ribas de Pouplana
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23 08010 Barcelona Spain
| | - Antoni Riera
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology Baldiri Reixac 10 08028 Barcelona Spain .,Departament de Química Inorgànica i Orgànica, Secció Química Orgànica. Universitat de Barcelona Martí i Franquès 1 08028 Barcelona Spain
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13
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Yuan SP, Dou PH, Jia YQ, Zhao JQ, You Y, Wang ZH, Zhou MQ, Yuan WC. Catalytic asymmetric aromatizing inverse electron-demand [4+2] cycloaddition of 1-thioaurones and 1-azaaurones. Chem Commun (Camb) 2021; 58:553-556. [PMID: 34908046 DOI: 10.1039/d1cc06357j] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Using 1-thioaurones and 1-azaaurones as electron-deficient oxa-dienes, an organocatalytic asymmetric aromatizing inverse electron-demand [4+2] cycloaddition with γ-deconjugated butenolides and azlactones was developed. A wide range of optically active benzothiophene-fused δ-lactones and indole-fused δ-lactones were obtained with desirable outcomes (up to 94% yield, >99 : 1 dr and 99% ee).
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Affiliation(s)
- Shu-Pei Yuan
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, China. .,Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pei-Hao Dou
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, China. .,Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yun-Qing Jia
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, China. .,Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jian-Qiang Zhao
- Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Yong You
- Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Zhen-Hua Wang
- Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Ming-Qiang Zhou
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, China.
| | - Wei-Cheng Yuan
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, China. .,Innovation Research Center of Chiral Drugs, Institute for Advanced Study, Chengdu University, Chengdu 610106, China
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14
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Yao Q, Gao S, Wu C, Lin T, Gao Y. Enzymatic non-covalent synthesis of supramolecular assemblies as a general platform for bioorthogonal prodrugs activation to combat drug resistance. Biomaterials 2021; 277:121119. [PMID: 34492583 DOI: 10.1016/j.biomaterials.2021.121119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/10/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022]
Abstract
Multi-drug resistance (MDR) is one of the leading causes of the anticancer failures. Besides the blockage of the MDR pathways, the development of more potent drugs is with urgent needs, but has been postponed mainly due to an imbalance between safety and efficacy. The recent development of the bioorthogonal prodrug activation strategy has shown immense potential to balance safety and efficacy, while recent studies only focused on few drug entities such as doxorubicin and monomethyl auristatin E, leaving the vast collection of toxins undetermined. Here we have enumerated typical molecular entities ranging from food and drug administration (FDA) approved drugs to a heated antibody drug conjugates (ADC) warhead and a trichothecene toxin to demonstrate that the bioorthogonal caging and specific activation could serve as a general design to increase the therapeutic index of bioactive molecules. These prodrugs can be efficiently activated on-demand by the bioorthogonal activators whose distribution was regulated by the cancer cell specific enzymatic non-covalent synthesis of supramolecular self-assemblies. The prodrug activation not only enhanced the synergistic therapeutic effect within a broad range of dose ratios but also allowed the convenient switching of drug identities to successfully combat MDR tumor in vivo. In general, this strategy might serve as a general platform, which can be readily applicable to enlarge the therapeutic window for various bioactive molecules. We envision that the spatiotemporal controlled bioorthogonal prodrug activation would facilitate the discovery of anticancer drugs.
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Affiliation(s)
- Qingxin Yao
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuo Gao
- Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, 361102, China
| | - Chengling Wu
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology, Beijing, 100190, China
| | - Ting Lin
- Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, 361102, China.
| | - Yuan Gao
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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15
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Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chem Rev 2021; 121:9816-9872. [PMID: 34232033 PMCID: PMC8391798 DOI: 10.1021/acs.chemrev.1c00107] [Citation(s) in RCA: 190] [Impact Index Per Article: 63.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
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Affiliation(s)
- John A. Keith
- Department
of Chemical and Petroleum Engineering Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Valentin Vassilev-Galindo
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Bingqing Cheng
- Accelerate
Programme for Scientific Discovery, Department
of Computer Science and Technology, 15 J. J. Thomson Avenue, Cambridge CB3 0FD, United Kingdom
| | - Stefan Chmiela
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Michael Gastegger
- Department
of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, 10587, Berlin, Germany
| | - Klaus-Robert Müller
- Machine
Learning Group, Technische Universität
Berlin, 10587, Berlin, Germany
- Department
of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Korea
- Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Alexandre Tkatchenko
- Department
of Physics and Materials Science, University
of Luxembourg, L-1511 Luxembourg City, Luxembourg
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16
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Shen LW, Li TT, You Y, Zhao JQ, Wang ZH, Yuan WC. Inverse Electron-Demand Aza-Diels-Alder Reaction of Cyclic Enamides with 1,2-Diaza-1,3-dienes in Situ Generated from α-Halogeno Hydrazones: Access to Fused Polycyclic Tetrahydropyridazine Derivatives. J Org Chem 2021; 86:11472-11481. [PMID: 34343003 DOI: 10.1021/acs.joc.1c00993] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
An efficient inverse electron-demand aza-Diels-Alder reaction of cyclic enamides and 1,2-diaza-1,3-dienes, which could be readily formed in situ from α-halogeno hydrazones and a base, has been successfully developed. With the developed approach, a wide range of fused polycyclic tetrahydropyridazines were smoothly obtained in up to 99% yield under benign reaction conditions. This reaction concept was also extended to acyclic enamide substrates for accessing 1,4,5,6-tetrahydropyridazines. A gram-scale experiment and further derivatizations of the polycyclic tetrahydropyridazine products were also conducted to verify the practicability of the methodology.
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Affiliation(s)
- Li-Wen Shen
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu, 610041, China.,Institute for Advanced Study, Chengdu University, Chengdu 610106, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ting-Ting Li
- National Engineering Research Center of Chiral Drugs, Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu, 610041, China.,Institute for Advanced Study, Chengdu University, Chengdu 610106, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yong You
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Jian-Qiang Zhao
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Zhen-Hua Wang
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Wei-Cheng Yuan
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
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17
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Ledovskaya MS, Polynski MV, Ananikov VP. One-Pot and Two-Chamber Methodologies for Using Acetylene Surrogates in the Synthesis of Pyridazines and Their D-Labeled Derivatives. Chem Asian J 2021; 16:2286-2297. [PMID: 34152671 DOI: 10.1002/asia.202100562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/18/2021] [Indexed: 01/03/2023]
Abstract
Acetylene surrogates are efficient tools in modern organic chemistry with largely unexplored potential in the construction of heterocyclic cores. Two novel synthetic paths to 3,6-disubstituted pyridazines were proposed using readily available acetylene surrogates through flexible C2 unit installation procedures in a common reaction space mode (one-pot) and distributed reaction space mode (two-chamber): (1) an interaction of 1,2,4,5-tetrazine and its acceptor-functionalized derivatives with a CaC2 -H2 O mixture performed in a two-chamber reactor led to the corresponding pyridazines in quantitative yields; (2) [4+2] cycloaddition of 1,2,4,5-tetrazines to benzyl vinyl ether can be considered a universal synthetic path to a wide range of pyridazines. Replacing water with D2 O and vinyl ether with its trideuterated analog in the developed procedures, a range of 4,5-dideuteropyridazines of 95-99% deuteration degree was synthesized for the first time. Quantum chemical modeling allowed to quantify the substituent effect in both synthetic pathways.
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Affiliation(s)
- Maria S Ledovskaya
- Institute of Chemistry, Saint Petersburg State University, Universitetsky prospect 26, Saint Petersburg, 198504, Russia
| | - Mikhail V Polynski
- Institute of Chemistry, Saint Petersburg State University, Universitetsky prospect 26, Saint Petersburg, 198504, Russia.,N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, Leninsky prospect 47, Moscow, 119991, Russia
| | - Valentine P Ananikov
- Institute of Chemistry, Saint Petersburg State University, Universitetsky prospect 26, Saint Petersburg, 198504, Russia.,N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, Leninsky prospect 47, Moscow, 119991, Russia
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18
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Abstract
Click chemistry has been established rapidly as one of the most valuable methods for the chemical transformation of complex molecules. Due to the rapid rates, clean conversions to the products, and compatibility of the reagents and reaction conditions even in complex settings, it has found applications in many molecule-oriented disciplines. From the vast landscape of click reactions, approaches have emerged in the past decade centered around oxidative processes to generate in situ highly reactive synthons from dormant functionalities. These approaches have led to some of the fastest click reactions know to date. Here, we review the various methods that can be used for such oxidation-induced "one-pot" click chemistry for the transformation of small molecules, materials, and biomolecules. A comprehensive overview is provided of oxidation conditions that induce a click reaction, and oxidation conditions are orthogonal to other click reactions so that sequential "click-oxidation-click" derivatization of molecules can be performed in one pot. Our review of the relevant literature shows that this strategy is emerging as a powerful approach for the preparation of high-performance materials and the generation of complex biomolecules. As such, we expect that oxidation-induced "one-pot" click chemistry will widen in scope substantially in the forthcoming years.
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Affiliation(s)
- Bauke Albada
- Laboratory of Organic Chemistry, Wageningen University & Research, Stippeneng 4, 6807 WE Wageningen, The Netherlands
| | - Jordi F Keijzer
- Laboratory of Organic Chemistry, Wageningen University & Research, Stippeneng 4, 6807 WE Wageningen, The Netherlands
| | - Han Zuilhof
- Laboratory of Organic Chemistry, Wageningen University & Research, Stippeneng 4, 6807 WE Wageningen, The Netherlands.,School of Pharmaceutical Sciences and Technology, Tianjin University, Tianjin 300072, China.,Department of Chemical and Materials Engineering, Faculty of Engineering, King Abdulaziz University, 21589 Jeddah, Saudi Arabia
| | - Floris van Delft
- Laboratory of Organic Chemistry, Wageningen University & Research, Stippeneng 4, 6807 WE Wageningen, The Netherlands.,Synaffix BV, Industrielaan 63, 5349 AE, Oss, The Netherlands
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19
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Yu S, Vermeeren P, Hamlin TA, Bickelhaupt FM. How Oriented External Electric Fields Modulate Reactivity. Chemistry 2021; 27:5683-5693. [PMID: 33289179 PMCID: PMC8049047 DOI: 10.1002/chem.202004906] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/04/2020] [Indexed: 01/27/2023]
Abstract
A judiciously oriented external electric field (OEEF) can catalyze a wide range of reactions and can even induce endo/exo stereoselectivity of cycloaddition reactions. The Diels-Alder reaction between cyclopentadiene and maleic anhydride is studied by using quantitative activation strain and Kohn-Sham molecular orbital theory to pinpoint the origin of these catalytic and stereoselective effects. Our quantitative model reveals that an OEEF along the reaction axis induces an enhanced electrostatic and orbital interaction between the reactants, which in turn lowers the reaction barrier. The stronger electrostatic interaction originates from an increased electron density difference between the reactants at the reactive center, and the enhanced orbital interaction arises from the promoted normal electron demand donor-acceptor interaction driven by the OEEF. An OEEF perpendicular to the plane of the reaction axis solely stabilizes the exo pathway of this reaction, whereas the endo pathway remains unaltered and efficiently steers the endo/exo stereoselectivity. The influence of the OEEF on the inverse electron demand Diels-Alder reaction is also investigated; unexpectedly, it inhibits the reaction, as the electric field now suppresses the critical inverse electron demand donor-acceptor interaction.
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Affiliation(s)
- Song Yu
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081HVAmsterdamThe Netherlands
| | - Pascal Vermeeren
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081HVAmsterdamThe Netherlands
| | - Trevor A. Hamlin
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081HVAmsterdamThe Netherlands
| | - F. Matthias Bickelhaupt
- Department of Theoretical ChemistryAmsterdam Institute of Molecular and Life Sciences (AIMMS)Amsterdam Center for Multiscale Modeling (ACMM)Vrije Universiteit AmsterdamDe Boelelaan 10831081HVAmsterdamThe Netherlands
- Institute for Molecules and Materials (IMM)Radboud UniversityHeyendaalseweg 1356525 AJNijmegenThe Netherlands
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20
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Abstract
As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until 'big data' applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barriers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.
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21
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Orlandi M, Escudero-Casao M, Licini G. Nucleophilicity Prediction via Multivariate Linear Regression Analysis. J Org Chem 2021; 86:3555-3564. [PMID: 33534569 PMCID: PMC7901016 DOI: 10.1021/acs.joc.0c02952] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
![]()
The concept of nucleophilicity is
at the basis of most transformations
in chemistry. Understanding and predicting the relative reactivity
of different nucleophiles is therefore of paramount importance. Mayr’s
nucleophilicity scale likely represents the most complete collection
of reactivity data, which currently includes over 1200 nucleophiles.
Several attempts have been made to theoretically predict Mayr’s
nucleophilicity parameters N based on calculation
of molecular properties, but a general model accounting for different
classes of nucleophiles could not be obtained so far. We herein show
that multivariate linear regression analysis is a suitable tool for
obtaining a simple model predicting N for virtually
any class of nucleophiles in different solvents for a set of 341 data
points. The key descriptors of the model were found to account for
the proton affinity, solvation energies, and sterics.
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Affiliation(s)
- Manuel Orlandi
- Department of Chemical Sciences, University of Padova, via Marzolo 1, 35131 Padova, Italy.,CIRCC-Consorzio Interuniversitario per le Reattività Chimiche e la Catalisi, Padova Unit, via Marzolo 1, 35131 Padova, Italy
| | - Margarita Escudero-Casao
- Department of Chemical Sciences, University of Padova, via Marzolo 1, 35131 Padova, Italy.,CIRCC-Consorzio Interuniversitario per le Reattività Chimiche e la Catalisi, Padova Unit, via Marzolo 1, 35131 Padova, Italy
| | - Giulia Licini
- Department of Chemical Sciences, University of Padova, via Marzolo 1, 35131 Padova, Italy.,CIRCC-Consorzio Interuniversitario per le Reattività Chimiche e la Catalisi, Padova Unit, via Marzolo 1, 35131 Padova, Italy
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22
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Shi Y, Prieto PL, Zepel T, Grunert S, Hein JE. Automated Experimentation Powers Data Science in Chemistry. Acc Chem Res 2021; 54:546-555. [PMID: 33471522 DOI: 10.1021/acs.accounts.0c00736] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.We propose three broad categories of data in chemistry: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liquid-dosing modules with computer vision to automate solubility screening, thereby gathering fundamental data that are necessary for almost every experimental design. Using computer vision offers the additional benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Experimental data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chemical processes. The third platform uses automated process analytical technology to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to determine exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.
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Affiliation(s)
- Yao Shi
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z3, Canada
| | - Paloma L. Prieto
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z3, Canada
| | - Tara Zepel
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z3, Canada
| | - Shad Grunert
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z3, Canada
| | - Jason E. Hein
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z3, Canada
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23
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Deb T, Tu J, Franzini RM. Mechanisms and Substituent Effects of Metal-Free Bioorthogonal Reactions. Chem Rev 2021; 121:6850-6914. [DOI: 10.1021/acs.chemrev.0c01013] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Titas Deb
- Department of Medicinal Chemistry, University of Utah, 30 S 2000 E, Salt Lake City, Utah 84112, United States
| | - Julian Tu
- Department of Medicinal Chemistry, University of Utah, 30 S 2000 E, Salt Lake City, Utah 84112, United States
| | - Raphael M. Franzini
- Department of Medicinal Chemistry, University of Utah, 30 S 2000 E, Salt Lake City, Utah 84112, United States
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24
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Jorner K, Brinck T, Norrby PO, Buttar D. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies. Chem Sci 2021; 12:1163-1175. [PMID: 36299676 PMCID: PMC9528810 DOI: 10.1039/d0sc04896h] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/02/2020] [Indexed: 12/19/2022] Open
Abstract
Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal mol−1 for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints. Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.![]()
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Affiliation(s)
- Kjell Jorner
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - Tore Brinck
- Applied Physical Chemistry
- Department of Chemistry
- CBH
- KTH Royal Institute of Technology
- Stockholm
| | - Per-Ola Norrby
- Data Science & Modelling
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Gothenburg
| | - David Buttar
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
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25
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Thakkar A, Johansson S, Jorner K, Buttar D, Reymond JL, Engkvist O. Artificial intelligence and automation in computer aided synthesis planning. REACT CHEM ENG 2021. [DOI: 10.1039/d0re00340a] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.
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Affiliation(s)
- Amol Thakkar
- Hit Discovery
- Discovery Sciences
- R&D
- AstraZeneca
- Gothenburg
| | | | - Kjell Jorner
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - David Buttar
- Early Chemical Development
- Pharmaceutical Sciences
- R&D
- AstraZeneca
- Macclesfield
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry
- University of Bern
- 3012 Bern
- Switzerland
| | - Ola Engkvist
- Hit Discovery
- Discovery Sciences
- R&D
- AstraZeneca
- Gothenburg
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26
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Lossouarn A, Renard PY, Sabot C. Tailored Bioorthogonal and Bioconjugate Chemistry: A Source of Inspiration for Developing Kinetic Target-Guided Synthesis Strategies. Bioconjug Chem 2020; 32:63-72. [PMID: 33232599 DOI: 10.1021/acs.bioconjchem.0c00568] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Kinetic target-guided synthesis (KTGS) is a promising tool for the discovery of biologically active compounds. It relies on the identification of potent ligands that are covalently assembled by the biological targets themselves from a pool of reagents. Significant effort is devoted to developing new KTGS strategies; however, only a handful of biocompatible reactions are available, which may be insufficient to meet the specificities (stability, dynamics, active site topology, etc.) of a wide range of biological targets with therapeutic potential. This Topical Review proposes a retrospective analysis of existing KTGS ligation tools, in terms of their kinetics and analogy with other biocompatible reactions, and provides new clues to expand the KTGS toolkit. By way of examples, a nonexhaustive selection of such chemical ligation tools belonging to different classes of reactions as promising candidate reactions for KTGS are suggested.
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Affiliation(s)
- Alexis Lossouarn
- Normandie Université, Centre National de la Recherche Scientifique, UNIROUEN, INSA Rouen, COBRA, UMR 6014 & FR 3038, 76000, Rouen, France
| | - Pierre-Yves Renard
- Normandie Université, Centre National de la Recherche Scientifique, UNIROUEN, INSA Rouen, COBRA, UMR 6014 & FR 3038, 76000, Rouen, France
| | - Cyrille Sabot
- Normandie Université, Centre National de la Recherche Scientifique, UNIROUEN, INSA Rouen, COBRA, UMR 6014 & FR 3038, 76000, Rouen, France
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27
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Zhao D, Kong Y, Zhao S, Xing H. Engineering Functional DNA–Protein Conjugates for Biosensing, Biomedical, and Nanoassembly Applications. Top Curr Chem (Cham) 2020; 378:41. [DOI: 10.1007/s41061-020-00305-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 05/05/2020] [Indexed: 12/31/2022]
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