Rapid evaluation of the interaction energies for hydrogen-bonded uracil and thymine dimers, trimers and tetramers

2014 ◽  
Vol 1048 ◽  
pp. 46-53 ◽  
Author(s):  
Xi-Chan Gao ◽  
Cui-Ying Huang ◽  
Chang-Sheng Wang
Keyword(s):  
Rapid Evaluation ◽  
Interaction Energies ◽  
Thymine Dimers ◽  
Hydrogen Bonded

Rapid evaluation of the interaction energies for carbohydrate-containing hydrogen-bonded complexes via the polarizable dipole–dipole interaction model combined with NBO or AM1 charge

RSC Advances ◽  
2015 ◽  
Vol 5 (9) ◽  
pp. 6452-6461 ◽  
Author(s):  
Jiao-Jiao Hao ◽  
Chang-Sheng Wang
Keyword(s):  
Hydrogen Bond ◽  
Interaction Model ◽  
Dipole Interaction ◽  
Rapid Evaluation ◽  
Interaction Energies ◽  
Bonded Complexes ◽  
Hydrogen Bonded

The polarizable dipole–dipole interaction model has been developed to rapidly and accurately estimate the hydrogen bond distances and interaction energies for carbohydrate-containing hydrogen-bonded complexes.

Rapid evaluation of the interaction energies for O–H···O hydrogen-bonded complexes

2014 ◽  
Vol 133 (8) ◽  
Author(s):  
Jiao-Jiao Hao ◽  
Shu-Shi Li ◽  
Xiao-Nan Jiang ◽  
Xiao-Lei Li ◽  
Chang-Sheng Wang
Keyword(s):  
Rapid Evaluation ◽  
Interaction Energies ◽  
Bonded Complexes ◽  
Hydrogen Bonded

Hydrogen-bonded structures and interaction energies in two forms of the SGLT-2 inhibitor sotagliflozin

2017 ◽  
Vol 73 (9) ◽  
pp. 718-723
Author(s):  
Thomas Gelbrich ◽  
Verena Adamer ◽  
Marijan Stefinovic ◽  
Andrea Thaler ◽  
Ulrich J. Griesser
Keyword(s):  
Intermolecular Interactions ◽  
Crystallographic Axis ◽  
Interaction Energies ◽  
Molecular Conformations ◽  
Oh Groups ◽  
Molecule Interaction ◽  
Hydrogen Bonded

The sotagliflozin molecule exhibits two fundamentally different molecular conformations in form 1 {systematic name: (2S,3R,4R,5S,6R)-2-[4-chloro-3-(4-ethoxybenzyl)phenyl]-6-(methylsulfanyl)tetrahydro-2H-pyran-3,4,5-triol, C21H25ClO5S, (I)} and the monohydrate [C21H25ClO5S·H2O, (II)]. Both crystals display hydrogen-bonded layers formed by intermolecular interactions which involve the three –OH groups of the xyloside fragment of the molecule. The layer architectures of (I) and (II) contain a non-hydrogen-bonded molecule–molecule interaction along the short crystallographic axis (a axis) whose total PIXEL energy exceeds that of each hydrogen-bonded molecule–molecule pair. The hydrogen-bonded layer of (I) has the topology of the 4-connected sql net and that formed by the water and sotagliflozin molecules of (II) has the topology of a 3,7-connected net.

scholarly journals AP-Net: An Atomic-Pairwise Neural Network for Smooth and Transferable Interaction Potentials

2020 ◽  
Author(s):  
Zachary Glick ◽  
Derek Metcalf ◽  
Alexios Koutsoukas ◽  
Steven Spronk ◽  
Daniel Cheney ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy Surfaces ◽  
Computational Cost ◽  
Absolute Error ◽  
Interaction Energies ◽  
Promising Alternative ◽  
Bond Model ◽  
Recent Emergence ◽  
Energy Surfaces ◽  
Hydrogen Bonded

<div> <div> <div> <p>Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using <i>ab initio</i> methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies, but also predict smooth and asymptotically correct potential energy surfaces. However, existing ML models are not guaranteed to obey these constraints. Indeed, systemic deficiencies are apparent in the predictions of our previous hydrogen-bond model as well as the popular ANI-1X model, which we attribute to the use of an atomic energy partition. As a solution, we propose an alternative atomic-pairwise framework specifically for intermolecular ML potentials, and we introduce AP-Net—a neural network model for interaction energies. The AP-Net model is developed using this physically motivated atomic-pairwise paradigm and also exploits the interpretability of symmetry adapted perturbation theory (SAPT). We show that in contrast to other models, AP-Net produces smooth, physically meaningful intermolecular potentials exhibiting correct asymptotic behavior. Initially trained on only a limited number of mostly hydrogen-bonded dimers, AP-Net makes accurate predictions across the chemically diverse S66x8 dataset, demonstrating significant transferability. On a test set including experimental hydrogen-bonded dimers, AP-Net predicts total interaction energies with a mean absolute error of 0.37 kcal mol−1, reducing errors by a factor of 2-5 across SAPT components from previous neural network potentials. The pairwise interaction energies of the model are physically interpretable, and an investigation of predicted electrostatic energies suggests that the model ‘learns’ the physics of hydrogen-bonded interactions. </p> </div> </div> </div>

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