Native Atom Types for Knowledge-Based Potentials:  Application to Binding Energy Prediction

2004 ◽  
Vol 47 (18) ◽  
pp. 4538-4558 ◽  
Author(s):  
Brian N. Dominy ◽  
Eugene I. Shakhnovich
Keyword(s):  
Binding Energy ◽  
Energy Prediction ◽  
Knowledge Based ◽  
Binding Energy Prediction

Virtual Screening of the Indonesian Medicinal Plant and Zinc Databases for Potential Inhibitors of the RNA-Dependent RNA Polymerase (RdRp) of 2019 Novel Coronavirus

2020 ◽  
Vol 20 (6) ◽  
pp. 1430
Author(s):  
Muhammad Arba ◽  
Andry Nur-Hidayat ◽  
Ida Usman ◽  
Arry Yanuar ◽  
Setyanto Tri Wahyudi ◽  
...  
Keyword(s):  
Binding Energy ◽  
Rna Polymerase ◽  
Antiviral Drug ◽  
Dynamics Simulation ◽  
Human Beings ◽  
Rna Dependent Rna Polymerase ◽  
Energy Prediction ◽  
Binding Energy Prediction ◽  
Solvation Energies ◽  
Novel Coronavirus

The novel coronavirus disease 19 (Covid-19) which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a pandemic across the world, which necessitate the need for the antiviral drug discovery. One of the potential protein targets for coronavirus treatment is RNA-dependent RNA polymerase. It is the key enzyme in the viral replication machinery, and it does not exist in human beings, therefore its targeting has been considered as a strategic approach. Here we describe the identification of potential hits from Indonesian Herbal and ZINC databases. The pharmacophore modeling was employed followed by molecular docking and dynamics simulation for 40 ns. 151 and 14480 hit molecules were retrieved from Indonesian herbal and ZINC databases, respectively. Three hits that were selected based on the structural analysis were stable during 40 ns, while binding energy prediction further implied that ZINC1529045114, ZINC169730811, and 9-Ribosyl-trans-zeatin had tighter binding affinities compared to Remdesivir. The ZINC169730811 had the strongest affinity toward RdRp compared to the other two hits including Remdesivir and its binding was corroborated by electrostatic, van der Waals, and nonpolar contribution for solvation energies. The present study offers three hits showing tighter binding to RdRp based on MM-PBSA binding energy prediction for further experimental verification.

MM/GBSA Binding Energy Prediction on the PDBbind Data Set: Successes, Failures, and Directions for Further Improvement

2012 ◽  
Vol 53 (1) ◽  
pp. 201-209 ◽  
Author(s):  
Paulette A. Greenidge ◽  
Christian Kramer ◽  
Jean-Christophe Mozziconacci ◽  
Romain M. Wolf
Keyword(s):  
Binding Energy ◽  
Data Set ◽  
Energy Prediction ◽  
Binding Energy Prediction

GENERALIZED HYBRID DERIVATIVE COUPLING MODEL FOR DEFORMED NUCLEI

2001 ◽  
Vol 10 (06) ◽  
pp. 475-482 ◽  
Author(s):  
PARNA MITRA ◽  
BINAY MALAKAR ◽  
G. GANGOPADHYAY
Keyword(s):  
Rare Earth ◽  
Binding Energy ◽  
Ground State ◽  
Coupling Model ◽  
Light Nuclei ◽  
Derivative Coupling ◽  
Deformed Nuclei ◽  
Energy Prediction ◽  
Binding Energy Prediction ◽  
Hybrid Derivative

The generalized hybrid derivative coupling model has been applied to explore various ground state properties of a number of deformed nuclei throughout the periodic table. In this work we have confined our calculation only to the model characterized by hybridization parameter α=1/4. For the light nuclei in the sd shell, the binding energy prediction is reasonably good while the results for charge radius, quadrupole moment and deformation are excellent. For the rare earth and actinide nuclei, the results in the heavier isotopes are reasonably good while the model fails in the lighter isotopes.

scholarly journals Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

2021 ◽  
Author(s):  
Dong Hyeon Mok ◽  
Seoin Back
Keyword(s):  
Atomic Structure ◽  
Density Functional ◽  
Nearest Neighbor ◽  
Catalyst Surface ◽  
Binding Energies ◽  
Reasonable Accuracy ◽  
Density Functional Calculation ◽  
Energy Prediction ◽  
Binding Energy Prediction ◽  
Free Representation

For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps.

scholarly journals Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

2021 ◽  
Author(s):  
Dong Hyeon Mok ◽  
Seoin Back
Keyword(s):  
Atomic Structure ◽  
Density Functional ◽  
Nearest Neighbor ◽  
Catalyst Surface ◽  
Binding Energies ◽  
Reasonable Accuracy ◽  
Density Functional Calculation ◽  
Energy Prediction ◽  
Binding Energy Prediction ◽  
Free Representation

For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps.

scholarly journals Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery

2021 ◽  
Author(s):  
Dong Hyeon Mok ◽  
Seoin Back
Keyword(s):  
Atomic Structure ◽  
Density Functional ◽  
Nearest Neighbor ◽  
Catalyst Surface ◽  
Binding Energies ◽  
Reasonable Accuracy ◽  
Density Functional Calculation ◽  
Energy Prediction ◽  
Binding Energy Prediction ◽  
Free Representation

For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps.

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