Gibbs energies of activation for reacting systems with multiple reactant‐state and transition‐state conformations

2022 ◽  
Author(s):  
Ian H. Williams
Keyword(s):  
Transition State ◽  
Gibbs Energies ◽  
Reacting Systems

scholarly journals Conformational Sampling for Transition State Searches on a Computational Budget

2021 ◽  
Author(s):  
Qiyuan Zhao ◽  
Hsuan-Hao Hsu ◽  
Brett Savoie
Keyword(s):  
Transition State ◽  
Molecular Conformation ◽  
Conformational Space ◽  
Relative Advantage ◽  
Conformational Sampling ◽  
Reaction Prediction ◽  
Computational Budget ◽  
Reacting Systems ◽  
High Level ◽  
The Cost

Transition state searches are the basis for characterizing reaction mechanisms and activation energies, and are thus central to myriad chemical applications. Nevertheless, common search algorithms are sensitive to molecular conformation and the conformational space of even medium-sized reacting systems are too complex to explore with brute force. Here we show that it is possible to train a classifier to learn the features of conformers that conduce successful transition state searches, such that optimal conformers can be down-selected before incurring the cost of a high-level transition state search. To this end, we have benchmarked the use of a modern conformational generation algorithm with our reaction prediction methodology, Yet Another Reaction Program (YARP), for reaction prediction tasks. We demonstrate that neglecting conformer contributions leads to qualitatively incorrect activation energy estimations for a broad range of reactions, whereas a simple random forest classifier can be used to reliably down-select low-barrier conformers. We also compare the relative advantage of performing conformational sampling on reactant, product, and putative transition state geometries. The robust performance of this relatively simple machine learning classifier mitigates cost as a factor when implementing conformational sampling into contemporary reaction prediction workflows.

Memapsin 2, a drug target for Alzheimer's disease

2003 ◽  
Vol 70 ◽  
pp. 213-220 ◽  
Author(s):  
Gerald Koelsch ◽  
Robert T. Turner ◽  
Lin Hong ◽  
Arun K. Ghosh ◽  
Jordan Tang
Keyword(s):  
Crystal Structure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transition State ◽  
Amyloid Precursor Protein ◽  
Therapeutic Target ◽  
Drug Target ◽  
Aspartic Protease ◽  
Precursor Protein ◽  
Memapsin 2

Mempasin 2, a ϐ-secretase, is the membrane-anchored aspartic protease that initiates the cleavage of amyloid precursor protein leading to the production of ϐ-amyloid and the onset of Alzheimer's disease. Thus memapsin 2 is a major therapeutic target for the development of inhibitor drugs for the disease. Many biochemical tools, such as the specificity and crystal structure, have been established and have led to the design of potent and relatively small transition-state inhibitors. Although developing a clinically viable mempasin 2 inhibitor remains challenging, progress to date renders hope that memapsin 2 inhibitors may ultimately be useful for therapeutic reduction of ϐ-amyloid.

Collisional dynamics of Ca1D + HBr reactions: evidence for transition-state motions

1999 ◽  
Vol 97 (8) ◽  
pp. 967-976 ◽  
Author(s):  
M. Garay Salazar, J. M. Orea Rocha, A.
Keyword(s):  
Transition State ◽  
Collisional Dynamics

Reactions of an Aluminium(I) Reagent with 1,2-, 1,3- and 1,5-Dienes: Dearomatisation, Reversibility, and a Pericyclic Mechanism

2019 ◽  
Author(s):  
Clare Bakewell ◽  
Martí Garçon ◽  
Richard Y Kong ◽  
Louisa O'Hare ◽  
Andrew J. P. White ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Dft Calculations ◽  
Transition State ◽  
Reaction Pathways ◽  
Aromatic Rings ◽  
Aromatic Character ◽  
Pericyclic Reaction

The reactions of an aluminium(I) reagent with a series of 1,2-, 1,3- and 1,5-dienes are reported. In the case of 1,3-dienes the reaction occurs by a pericyclic reaction mechanism, specifically a cheletropic cycloaddition, to form aluminocyclopentene containing products. This mechanism has been interrogated by stereochemical experiments and DFT calculations. The stereochemical experiments show that the (4+1) cycloaddition follows a suprafacial topology, while calculations support a concerted albeit asynchronous pathway in which the transition state demonstrates aromatic character. Remarkably, the substrate scope of the (4+1) cycloaddition includes dienes that are either in part, or entirely, contained within aromatic rings. In these cases, reactions occur with dearomatisation of the substrate and can be reversible. In the case of 1,2- or 1,5-dienes complementary reactivity is observed; the orthogonal nature of the C=C π-bonds (1,2-diene) and the homoconjugated system (1,5-diene) both disfavour a (4+1) cycloaddition. Rather, reaction pathways are determined by an initial (2+1) cycloaddition to form an aluminocyclopropane intermediate which can in turn undergo insertion of a further C=C π-bond leading to complex organometallic products that incorporate fused hydrocarbon rings.

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

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