Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction

2019 ◽  
Vol 10 (18) ◽  
pp. 5640-5646 ◽  
Author(s):  
Jiechun Liang ◽  
Xi Zhu
Keyword(s):  
Machine Learning ◽  
Binding Energy ◽  
Band Gap ◽  
Exciton Binding Energy ◽  
Energy Prediction ◽  
Binding Energy Prediction

Tuning the binding energy of excitons in the MoS2 monolayer by molecular functionalization and defective engineering

2020 ◽  
Vol 22 (21) ◽  
pp. 11936-11942
Author(s):  
Kangli Wang ◽  
Beate Paulus
Keyword(s):  
Absorption Spectrum ◽  
Binding Energy ◽  
Optical Absorption ◽  
Band Gap ◽  
Optical Absorption Spectrum ◽  
Exciton Binding Energy ◽  
Electronic Band ◽  
Mos2 Monolayer ◽  
Electronic Band Gap

Using the DFT-GW-BSE method, we analyze how the electronic band gap, optical absorption spectrum and exciton binding energy of the MoS2 monolayer are influenced by NO and C3H3N3 molecules and S-defects.

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.

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