Literature DB >> 26580168

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.

Dmitrij Rappoport1, Cooper J Galvin2, Dmitry Yu Zubarev1, Alán Aspuru-Guzik1.   

Abstract

While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

Entities:  

Year:  2014        PMID: 26580168     DOI: 10.1021/ct401004r

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  13 in total

1.  Locating landmarks on high-dimensional free energy surfaces.

Authors:  Ming Chen; Tang-Qing Yu; Mark E Tuckerman
Journal:  Proc Natl Acad Sci U S A       Date:  2015-03-03       Impact factor: 11.205

2.  An intermediate level of abstraction for computational systems chemistry.

Authors:  Jakob L Andersen; Christoph Flamm; Daniel Merkle; Peter F Stadler
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2017-12-28       Impact factor: 4.226

Review 3.  Enhanced semiempirical QM methods for biomolecular interactions.

Authors:  Nusret Duygu Yilmazer; Martin Korth
Journal:  Comput Struct Biotechnol J       Date:  2015-02-28       Impact factor: 7.271

4.  Discovering chemistry with an ab initio nanoreactor.

Authors:  Lee-Ping Wang; Alexey Titov; Robert McGibbon; Fang Liu; Vijay S Pande; Todd J Martínez
Journal:  Nat Chem       Date:  2014-11-02       Impact factor: 24.427

5.  Neural Networks for the Prediction of Organic Chemistry Reactions.

Authors:  Jennifer N Wei; David Duvenaud; Alán Aspuru-Guzik
Journal:  ACS Cent Sci       Date:  2016-10-14       Impact factor: 14.553

6.  Implementation and performance of the artificial force induced reaction method in the GRRM17 program.

Authors:  Satoshi Maeda; Yu Harabuchi; Makito Takagi; Kenichiro Saita; Kimichi Suzuki; Tomoya Ichino; Yosuke Sumiya; Kanami Sugiyama; Yuriko Ono
Journal:  J Comput Chem       Date:  2017-11-14       Impact factor: 3.376

7.  Efficient prediction of reaction paths through molecular graph and reaction network analysis.

Authors:  Yeonjoon Kim; Jin Woo Kim; Zeehyo Kim; Woo Youn Kim
Journal:  Chem Sci       Date:  2017-12-12       Impact factor: 9.825

Review 8.  A Trajectory-Based Method to Explore Reaction Mechanisms.

Authors:  Saulo A Vázquez; Xose L Otero; Emilio Martinez-Nunez
Journal:  Molecules       Date:  2018-11-30       Impact factor: 4.411

9.  Insights Into Chemical Reactions at the Beginning of the Universe: From HeH+ to H3.

Authors:  Soumya Ranjan Dash; Tamal Das; Kumar Vanka
Journal:  Front Chem       Date:  2021-06-18       Impact factor: 5.221

10.  Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

Authors:  Bowen Liu; Bharath Ramsundar; Prasad Kawthekar; Jade Shi; Joseph Gomes; Quang Luu Nguyen; Stephen Ho; Jack Sloane; Paul Wender; Vijay Pande
Journal:  ACS Cent Sci       Date:  2017-09-05       Impact factor: 18.728

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