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Review

“Dividing and Conquering” and “Caching” in Molecular Modeling

by 1 and 1,2,*
1
School of Life Sciences, Jilin University, Changchun 130012, China
2
School of Artificial Intelligence, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Academic Editor: Małgorzata Borówko
Int. J. Mol. Sci. 2021, 22(9), 5053; https://doi.org/10.3390/ijms22095053
Received: 3 March 2021 / Revised: 26 April 2021 / Accepted: 27 April 2021 / Published: 10 May 2021
(This article belongs to the Special Issue Advances in Molecular Simulation)
Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems. View Full-Text
Keywords: molecular modeling; multiscale; coarse graining; molecular dynamics simulation; Monte Carlo simulation; force fields; neural network; many body interactions; sampling; local sampling; local free energy landscape; generalized solvation free energy molecular modeling; multiscale; coarse graining; molecular dynamics simulation; Monte Carlo simulation; force fields; neural network; many body interactions; sampling; local sampling; local free energy landscape; generalized solvation free energy
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MDPI and ACS Style

Cao, X.; Tian, P. “Dividing and Conquering” and “Caching” in Molecular Modeling. Int. J. Mol. Sci. 2021, 22, 5053. https://doi.org/10.3390/ijms22095053

AMA Style

Cao X, Tian P. “Dividing and Conquering” and “Caching” in Molecular Modeling. International Journal of Molecular Sciences. 2021; 22(9):5053. https://doi.org/10.3390/ijms22095053

Chicago/Turabian Style

Cao, Xiaoyong, and Pu Tian. 2021. "“Dividing and Conquering” and “Caching” in Molecular Modeling" International Journal of Molecular Sciences 22, no. 9: 5053. https://doi.org/10.3390/ijms22095053

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