Why pay more? QTAIM descriptors of non-covalent interactions in S22 from promolecular electron density

被引:3
|
作者
Ananyev, Ivan, V [1 ]
Fershtat, Leonid L. [1 ]
机构
[1] Russian Acad Sci, ND Zelinsky Inst Organ Chem, Moscow 119991, Russia
关键词
electron density; QTAIM; topological analysis; promolecule; CORRELATED MOLECULAR CALCULATIONS; ENZYME-SUBSTRATE COMPLEXES; ACCURATE DIFFRACTION DATA; GAUSSIAN-BASIS SETS; POPULATION ANALYSIS; QUANTUM TOPOLOGY; CHARGE; ATOM; REFINEMENTS; DATABASE;
D O I
10.1016/j.mencom.2023.10.022
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Based on the comparative analysis of electron density topological features obtained by different methods for the model set of bimolecular associates it is shown that the simplest promolecule model can be an efficient tool in studies of non-covalent interactions.
引用
收藏
页码:806 / 808
页数:3
相关论文
共 23 条
  • [1] Multiresolution non-covalent interaction analysis for ligand–protein promolecular electron density distributions
    L. Leherte
    Theoretical Chemistry Accounts, 2021, 140
  • [2] Correction to: Multiresolution non-covalent interaction analysis for ligand–protein promolecular electron density distributions
    L. Leherte
    Theoretical Chemistry Accounts, 2021, 140
  • [3] Multiresolution non-covalent interaction analysis for ligand-protein promolecular electron density distributions
    Leherte, L.
    THEORETICAL CHEMISTRY ACCOUNTS, 2021, 140 (01)
  • [4] Energetics of non-covalent interactions from electron and energy density distributions
    Saleh, Gabriele
    Gatti, Carlo
    Lo Presti, Leonardo
    COMPUTATIONAL AND THEORETICAL CHEMISTRY, 2015, 1053 : 53 - 59
  • [5] Non-covalent interaction energies of the S22 set and the correlation consistent Composite Approach (ccCA)
    Tekarly, Sammer
    Wilson, Angela K.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 243
  • [6] Evaluation of the effective fragment potential method using S22 dataset of non-covalent complexes
    Flick, Joanna C.
    Kosenkov, Dmytro
    Sherrill, C. David
    Slipchenko, Lyudmila V.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2011, 242
  • [7] A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases
    Ting Gao
    Hongzhi Li
    Wenze Li
    Lin Li
    Chao Fang
    Hui Li
    LiHong Hu
    Yinghua Lu
    Zhong-Min Su
    Journal of Cheminformatics, 8
  • [8] A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases
    Gao, Ting
    Li, Hongzhi
    Li, Wenze
    Li, Lin
    Fang, Chao
    Li, Hui
    Hu, LiHong
    Lu, Yinghua
    Su, Zhong-Min
    JOURNAL OF CHEMINFORMATICS, 2016, 8
  • [9] Multiresolution non-covalent interaction analysis for ligand-protein promolecular electron density distributions (vol 140, 9, 2021)
    Leherte, L.
    THEORETICAL CHEMISTRY ACCOUNTS, 2021, 140 (07)
  • [10] Why are S-F and S-O non-covalent interactions stabilising?
    Thorley, Karl J.
    McCulloch, Iain
    JOURNAL OF MATERIALS CHEMISTRY C, 2018, 6 (45) : 12413 - 12421