Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

2021 ◽  
Author(s):  
Siwei Song ◽  
Fang Chen ◽  
Yi Wang ◽  
Kangcai Wang ◽  
Mi Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Experimental Approach ◽  
Chemical Data ◽  
Research Paradigm ◽  
New Materials ◽  
High Throughput Virtual Screening

With the growth of chemical data, computation power and algorithms, machine learning-assisted high-throughput virtual screening (ML-assisted HTVS) is revolutionizing the research paradigm of new materials. Herein, a combined ML-assisted HTVS...

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

2016 ◽  
Vol 15 (10) ◽  
pp. 1120-1127 ◽  
Author(s):  
Rafael Gómez-Bombarelli ◽  
Jorge Aguilera-Iparraguirre ◽  
Timothy D. Hirzel ◽  
David Duvenaud ◽  
Dougal Maclaurin ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Experimental Approach ◽  
Light Emitting Diodes ◽  
Organic Light Emitting Diodes ◽  
Light Emitting ◽  
Organic Light ◽  
High Throughput Virtual Screening

scholarly journals Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach

2019 ◽  
Vol 151 (12) ◽  
pp. 121102 ◽  
Author(s):  
Collin F. Perkinson ◽  
Daniel P. Tabor ◽  
Markus Einzinger ◽  
Dennis Sheberla ◽  
Hendrik Utzat ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Experimental Approach ◽  
Singlet Exciton ◽  
Singlet Exciton Fission ◽  
High Throughput Virtual Screening

scholarly journals MEMES: Machine learning framework for Enhanced MolEcular Screening

2021 ◽  
Author(s):  
Sarvesh Mehta ◽  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
Aaftaab Sethi ◽  
Mallika Alvala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput ◽  
Molecular Screening ◽  
Learning Framework ◽  
High Throughput Virtual Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule...

Novel thermally activated delayed fluorescence materials by high-throughput virtual screening: going beyond donor–acceptor design

2021 ◽  
Vol 9 (9) ◽  
pp. 3324-3333 ◽  
Author(s):  
Ke Zhao ◽  
Ömer H. Omar ◽  
Tahereh Nematiaram ◽  
Daniele Padula ◽  
Alessandro Troisi
Keyword(s):  
Virtual Screening ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Delayed Fluorescence ◽  
Thermally Activated Delayed Fluorescence ◽  
Thermally Activated ◽  
Quantum Chemistry Calculations ◽  
Donor Acceptor ◽  
Molecular Semiconductors ◽  
High Throughput Virtual Screening

125 potential TADF candidates are identified through quantum chemistry calculations of 700 molecules derived from a database of 40 000 molecular semiconductors. Most of them are new and some do not belong to the class of donor–acceptor molecules.

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

scholarly journals High-Throughput Virtual Screening for a New Class of Antagonist Targeting LasR of Pseudomonas aeruginosa

ACS Omega ◽  
2021 ◽  
Author(s):  
Aishwarya Vetrivel ◽  
Santhi Natchimuthu ◽  
Vidyalakshmi Subramanian ◽  
Rajeswari Murugesan
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virtual Screening ◽  
High Throughput ◽  
New Class ◽  
High Throughput Virtual Screening

scholarly journals Structure-based high throughput virtual screening for drug discovery

2002 ◽  
Vol 58 (s1) ◽  
pp. c67-c67
Author(s):  
H. Jiang ◽  
J. Shen ◽  
X. Luo ◽  
H. Liu ◽  
F. Chen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Virtual Screening

scholarly journals Natural products as inhibitors of COVID-19 main protease – A virtual screening by molecular docking

2020 ◽  
Author(s):  
Marzieh omrani ◽  
Mohammad Bayati ◽  
Parvaneh Mehrbod ◽  
Samad Nejad-Ebrahimi
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Throughput ◽  
Herbal Medicines ◽  
Respiratory Illness ◽  
Fast Method ◽  
Main Protease ◽  
Glide Docking ◽  
Adme Properties ◽  
High Throughput Virtual Screening

Abstract Background: The novel coronavirus (2019-nCoV) causes a severe respiratory illness that was unknown in the human before. Its alarmingly quick transmission to many countries across the world resulted in a worldwide health emergency. It has caused a notable percentage of morbidity and mortality. Therefore, an imminent need for drugs to combat this disease has been increased. Global collaborative efforts from scientists are underway to find a therapy to treat infections and reduce death cases. Herbal medicines and purified natural products have been reported to have antiviral activity against Coronaviruses (CoVs).Methods: In this study, a High Throughput Virtual Screening (HTVS) protocol was used as a fast method on the discovery of novel drug candidates as the COVID-19 main protease inhibitors. Over 180,000 natural product-based compounds were obtained from the ZINC database and virtually screened against the COVID-19 main protease. In this study, the Glide docking program was applied for high throughput virtual screening. Extra precision (XP) and in a combination of Prime module, induced-fit docking (IFD) approach was also used. Additionally, the ADME properties of all compounds were analyzed, and the final selection was carried out based on the Lipinski rule of five. Results: The nineteen compounds were selected and introduced as new potential inhibitors. The compound ZINC08765174 (1-[3-(1H-indol-3-yl) propanoyl]-N-(4-phenylbutan-2-yl)piperidine-3-carboxamide) showed a strong binding affinity (-11.5 kcal/mol) to the crucial residues of COVID-19 main protease comparing to peramivir (-9.8 kcal/mol) as a positive control.Conclusions: The excellent ADME properties proposed the opportunity of this compound to be a promising candidate for the treatment of COVID-19.

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