The chemistry of stabilized clusters

1982 ◽  
Vol 308 (1501) ◽  
pp. 27-34 ◽  
Keyword(s):  
Electron Donor ◽  
Organic Molecules ◽  
Metal Cluster ◽  
Organic Substrates ◽  
Insertion Reactions ◽  
Donor Ligand ◽  
Small Organic Molecules ◽  
Wide Range ◽  
Metal Metal ◽  
High Yields

Studies of the chemistry of metal cluster complexes and, in particular, their reactions with small organic molecules, have been confined to relatively few systems. Among the reasons for this are: (i) not many clusters are easily synthesized in high yields; (ii) their reactions often give a multitude of products that are difficult to separate and characterize; (iii) the conditions required to bring about reactions often lead to fragmentation of the cluster into lower nuclearity (often mononuclear) species. One cluster whose chemistry has been extensively studied is [Os 3 H 2 (CO) 10 ]. This can be synthesized in high yields from [Os 3 (CO) 12 ] + H 2 (Knox et al. 1975) and reacts readily under mild conditions with a wide range of electron-donor molecules by virtue of its coordinative unsaturation (Shapley et al. 1975; Deeming & Hasso 1976; Adams & Golembeski 1979). Formally, one may consider that a metal—metal double bond is present, which is reduced to a single bond on coordination of an additional two-electron donor ligand such as an organophosphine. The presence of metal—hydrogen bonds in this cluster and the cluster’s ability to coordinate organic substrates enable it to undergo a wide variety of insertion reactions, leading to products that may be regarded as intermediates in the reduction of organic molecules by clusters (Deeming & Hasso 1975; Keister & Shapley 1975).

scholarly journals Organocatalysts for enantioselective synthesis of fine chemicals: definitions, trends and developments

2015 ◽  
Author(s):  
Chiara Palumbo
Keyword(s):  
Asymmetric Catalysis ◽  
Scientific Community ◽  
Organic Molecules ◽  
Optically Active ◽  
Enantioselective Synthesis ◽  
Fine Chemicals ◽  
Organic Transformations ◽  
Small Organic Molecules ◽  
Substrate Activation ◽  
High Yields

Organocatalysis, that is the use of small organic molecules to catalyse organic transformations, has been included among the most successful concepts in asymmetric catalysis and it has been used for the enantioselective construction of C-C, C-N, C-O, C-S, C-P, and C-halide bonds. Since the seminal works in early 2000, the scientific community has been paying an ever-growing attention to the use of organocatalysts for the synthesis, with high yields and remarkable stereoselectivities, of optically active fine chemicals of interest for the pharmaceutical industry. A brief overview is here presented about the two main classes of substrate activation by the catalyst: covalent organocatalysis and non-covalent organocatalysis, with a more stringent focus on some recent outcomes in the field of the latter and of hydrogen-bond-based catalysis. Finally, some successful examples of heterogenisation of organocatalysts are also discussed, in the view of a potential industrial exploitation.

scholarly journals Organocatalysts for enantioselective synthesis of fine chemicals: definitions, trends and developments

2015 ◽  
Keyword(s):  
Asymmetric Catalysis ◽  
Scientific Community ◽  
Organic Molecules ◽  
Optically Active ◽  
Enantioselective Synthesis ◽  
Fine Chemicals ◽  
Organic Transformations ◽  
Small Organic Molecules ◽  
Substrate Activation ◽  
High Yields

<p>Organocatalysis, that is the use of small organic molecules to catalyse organic transformations, has been included among the most successful concepts in asymmetric catalysis and it has been used for the enantioselective construction of C-C, C-N, C-O, C-S, C-P, and C-halide bonds. Since the seminal works in early 2000, the scientific community has been paying an ever-growing attention to the use of organocatalysts for the synthesis, with high yields and remarkable stereoselectivities, of optically active fine chemicals of interest for the pharmaceutical industry. A brief overview is here presented about the two main classes of substrate activation by the catalyst: covalent organocatalysis and non-covalent organocatalysis, with a more stringent focus on some recent outcomes in the field of the latter and of hydrogen-bond-based catalysis. Finally, some successful examples of heterogenisation of organocatalysts are also discussed, in the view of a potential industrial exploitation.</p>

scholarly journals Organocatalysts for enantioselective synthesis of fine chemicals: definitions, trends and developments

2015 ◽  
Author(s):  
Matteo Guidotti ◽  
Chiara Palumbo
Keyword(s):  
Asymmetric Catalysis ◽  
Organic Molecules ◽  
Optically Active ◽  
Fine Chemicals ◽  
Organic Transformations ◽  
Small Organic Molecules ◽  
Non Covalent Interactions ◽  
High Yields ◽  
Covalent Interactions ◽  
Covalent Activation

Abstract Organocatalysis, that is the use of small organic molecules to catalyze organic transformations, has been included among the most successful concepts in asymmetric catalysis, and it has been used for the enantioselective construction of C–C, C–N, C–O, C–S, C–P and C–halide bonds. Since the seminal works in early 2000, the scientific community has been paying an ever-growing attention to the use of organocatalysts for the synthesis, with high yields and remarkable stereoselectivities, of optically active fine chemicals of interest for the pharmaceutical industry. A brief overview is here presented about the two main classes of organocatalysis which are respectively characterized by covalent and non-covalent activation of the substrate. More detailed information about non-covalent interactions for organocatalysis are given. Finally, some successful examples of heterogenisation of organocatalysts are also discussed, in the view of a potential industrial exploitation.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

2020 ◽  
Author(s):  
Christian Devereux ◽  
Justin Smith ◽  
Kate Davis ◽  
Kipton Barros ◽  
Roman Zubatyuk ◽  
...  
Keyword(s):  
Drug Development ◽  
Autonomous Vehicles ◽  
Potential Energy Surfaces ◽  
Organic Molecules ◽  
Chemical Elements ◽  
Molecular Potential ◽  
Wide Range ◽  
Energy Surfaces ◽  
New Applications ◽  
Physical Phenomena

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>

Stereospecific, Palladium-catalyzed C(sp3)–H Alkenylation and Alkynylation of a Proline Derivative Enabled by 8-Aminoquinoline as a Directing Group

2020 ◽  
Author(s):  
Aleksandra Balliu ◽  
Aaltje Roelofje Femmigje Strijker ◽  
Michael Oschmann ◽  
Monireh Pourghasemi Lati ◽  
Oscar Verho
Keyword(s):  
Carboxylic Acid ◽  
Building Blocks ◽  
Wide Range ◽  
Palladium Catalyzed ◽  
Directing Group ◽  
High Yields ◽  
Vinyl Iodides ◽  
Initial Results

<p>In this preprint, we present our initial results concerning a stereospecific Pd-catalyzed protocol for the C3 alkenylation and alkynylation of a proline derivative carrying the well utilized 8‑aminoquinoline directing group. Efficient C–H alkenylation was achieved with a wide range of vinyl iodides bearing different aliphatic, aromatic and heteroaromatic substituents, to furnish the corresponding C3 alkenylated products in good to high yields. In addition, we were able show that this protocol can also be used to install an alkynyl group into the pyrrolidine scaffold, when a TIPS-protected alkynyl bromide was used as the reaction partner. Furthermore, two different methods for the removal of the 8-aminoquinoline auxiliary are reported, which can enable access to both <i>cis</i>- and <i>trans</i>-configured carboxylic acid building blocks from the C–H alkenylation products.</p>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

Synthetic and Mechanistic Studies of a Versatile Heteroaryl Thioether Directing Group for Pd(II) Catalysis

2019 ◽  
Author(s):  
Andrew Romine ◽  
Kin Yang ◽  
Malkanthi Karunananda ◽  
Jason Chen ◽  
Keary Engle
Keyword(s):  
Mechanistic Studies ◽  
Salvianolic Acid ◽  
Wide Range ◽  
Computational Data ◽  
Synthetic Utility ◽  
Directing Group ◽  
High Yields ◽  
Julia Olefination ◽  
Reaction Progress Kinetic Analysis

A weakly coordinating monodentate heteroaryl thioether directing group has been developed for use in Pd(II) catalysis to orchestrate key elementary steps in the catalytic cycle that require conformational flexibility in a manner that is difficult to accomplish with traditional strongly coordinating directing groups. This benzothiazole thioether, (BT)S, directing group can be used to promote oxidative Heck reactivity of internal alkenes providing a wide range of products in moderate to high yields. To demonstrate the broad applicability of this directing group, arene C–H olefination was also successfully developed. Reaction progress kinetic analysis provides insights into the role of the directing group in each reaction, which is supplemented with computational data for the oxidative Heck reaction. Furthermore, this (BT)S directing group can be transformed into a number of synthetically useful functional groups, including a sulfone for Julia olefination, allowing it to serve as a “masked olefin” directing group in synthetic planning. In order to demonstrate this synthetic utility, natural products (+)-salvianolic acid A and salvianolic acid F are formally synthesized using the (BT)S directed C–H olefination as the key step.

Sign in / Sign up

Export Citation Format

Share Document