Inorganic Nanotubes Stabilized by Ion Size Asymmetry:  Energy Calculations for AgI Clusters

2004 ◽  
Vol 108 (24) ◽  
pp. 8412-8418 ◽  
Author(s):  
Adam Wootton ◽  
Peter Harrowell
Keyword(s):  
Inorganic Nanotubes ◽  
Energy Calculations ◽  
Asymmetry Energy ◽  
Ion Size ◽  
Size Asymmetry

scholarly journals Mesoscale ordering in binary aqueous solvents induced by ion size asymmetry

Soft Matter ◽  
2016 ◽  
Vol 12 (21) ◽  
pp. 4778-4782 ◽  
Author(s):  
Monika Witala ◽  
Sebastian Lages ◽  
Kim Nygård
Keyword(s):  
Ion Size ◽  
Size Asymmetry

Influence of ion size asymmetry on the properties of ionic liquid–vapour interfaces

2005 ◽  
Vol 17 (45) ◽  
pp. S3301-S3307 ◽  
Author(s):  
Fernando Bresme ◽  
Minerva González-Melchor ◽  
José Alejandre
Keyword(s):  
Ionic Liquid ◽  
Ion Size ◽  
Size Asymmetry

The annealed (0001) α-alumina surface and its influence on thin-film growth

1995 ◽  
Vol 53 ◽  
pp. 336-337
Author(s):  
Michael W. Bench ◽  
Paul G. Kotula ◽  
C. Barry Carter
Keyword(s):  
Electron Microscopy ◽  
Surface Energy ◽  
Film Growth ◽  
Thin Film Growth ◽  
Energy Calculations ◽  
Transmission Electron ◽  
Reflection Electron Microscopy ◽  
Low Energy Electron ◽  
Surface Nature

The growth of semiconductors, superconductors, metals, and other insulators has been investigated using alumina substrates in a variety of orientations. The surface state of the alumina (for example surface reconstruction and step nature) can be expected to affect the growth nature and quality of the epilayers. As such, the surface nature has been studied using a number of techniques including low energy electron diffraction (LEED), reflection electron microscopy (REM), transmission electron microscopy (TEM), molecular dynamics computer simulations, and also by theoretical surface energy calculations. In the (0001) orientation, the bulk alumina lattice can be thought of as a layered structure with A1-A1-O stacking. This gives three possible terminations of the bulk alumina lattice, with theoretical surface energy calculations suggesting that termination should occur between the Al layers. Thus, the lattice often has been described as being made up of layers of (Al-O-Al) unit stacking sequences. There is a 180° rotation in the surface symmetry of successive layers and a total of six layers are required to form the alumina unit cell.

CO2 and CO monolayers on MgO(100) : LEED experiments and potential energy calculations

1994 ◽  
Vol 4 (6) ◽  
pp. 905-920 ◽  
Author(s):  
V. Panella ◽  
J. Suzanne ◽  
P. N. M. Hoang ◽  
C. Girardet
Keyword(s):  
Potential Energy ◽  
Energy Calculations ◽  
Potential Energy Calculations

POTENTIAL ENERGY CALCULATIONS ON POLYACETYLENE

1983 ◽  
Vol 44 (C3) ◽  
pp. C3-447-C3-450
Author(s):  
E. Cernia ◽  
L. D'Ilario ◽  
G. Nencini
Keyword(s):  
Potential Energy ◽  
Energy Calculations ◽  
Potential Energy Calculations

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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