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Introducing KICK-MEP: exploring potential energy surfaces in systems with significant non-covalent interactions.

Authors :
García-Argote, Williams
Ruiz, Lina
Inostroza, Diego
Cardenas, Carlos
Yañez, Osvaldo
Tiznado, William
Source :
Journal of Molecular Modeling. Nov2024, Vol. 30 Issue 11, p1-14. 14p.
Publication Year :
2024

Abstract

Context: Exploring potential energy surfaces (PES) is fundamental in computational chemistry, as it provides insights into the relationship between molecular energy, geometry, and chemical reactivity. We introduce Kick-MEP, a hybrid method for exploring the PES of atomic and molecular clusters, particularly those dominated by non-covalent interactions. Kick-MEP computes the Coulomb integral between the maximum and minimum electrostatic potential values on a 0.001 a.u. electron density isosurface for two interacting fragments. This approach efficiently estimates interaction energies and selects low-energy configurations at reduced computational cost. Kick-MEP was evaluated on silicon-lithium clusters, water clusters, and thymol encapsulated within Cucurbit[7]uril, consistently identifying the lowest energy structures, including global minima and relevant local minima. Methods: Kick-MEP generates an initial population of molecular structures using the stochastic Kick algorithm, which combines two molecular fragments (A and B). The molecular electrostatic potential (MEP) values on a 0.001 a.u. electron density isosurface for each fragment are used to compute the Coulomb integral between them. Structures with the lowest Coulomb integral are selected and refined through gradient-based optimization and DFT calculations at the PBE0-D3/Def2-TZVP level. Molecular docking simulations for the thymol-Cucurbit[7]uril complex using AutoDock Vina were performed for benchmarking. Kick-MEP was validated across different molecular systems, demonstrating its effectiveness in identifying the lowest energy structures, including global minima and relevant local minima, while maintaining a low computational cost. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16102940
Volume :
30
Issue :
11
Database :
Academic Search Index
Journal :
Journal of Molecular Modeling
Publication Type :
Academic Journal
Accession number :
180932635
Full Text :
https://doi.org/10.1007/s00894-024-06155-0