scholarly journals Molecular docking-based computational platform for high-throughput virtual screening

2022 ◽  
Author(s):  
Baohua Zhang ◽  
Hui Li ◽  
Kunqian Yu ◽  
Zhong Jin
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
High Throughput ◽  
Computational Platform ◽  
High Throughput Virtual Screening

scholarly journals High throughput screening of tubulin inhibitors from parasitic fungi

1970 ◽  
pp. 319-322
Author(s):  
P. A. Karpov ◽  
O. M. Demchuk ◽  
S. P. Ozheriedov ◽  
S. I. Spivak ◽  
O. V. Raievskyi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Screening ◽  
Protein Structures ◽  
Pathogenic Fungi ◽  
Tubulin Inhibitors ◽  
Parasitic Fungi ◽  
High Throughput Virtual Screening

Aim. Implementation of 3D-modeling, molecular dynamics, high-throughput screening and molecular docking for search of new inhibitors of parasitic fungi tubulin. Methods. Protein structures were constructed using I-TASSER server and optimized by Gromacs. Ligands library was prepared in Mopac7 program and screened using UCSF Dock 6. Best ligands were docked in CCDC Gold. Results. It was reconstructed spatial molecular structure for 93 α-, 95 β- and 78 γ-tubulins from 76 species of pathogenic fungi genus: Microsporum, Arthroderma, Histoplasma, Blastomyces, Emmonsia, Uncinocarpus, Coccidioides, Paracoccidioides, Aspergillus, Botrytis cinerea, Sclerotinia, Rhynchosporium, Marssonina, Scedosporium, Fusarium, Gibberella, Candida, Ceraceosorus, Malassezia, Anthracocystis, Melanopsichium, Sporisorium, Ustilago, Cryptococcus, Trichosporon, Mucor, Rhizopus and Lichtheimia. Libraries of 3D-models of parasitic fungi tubulins and perspective ligands were created. Based on results of high-throughput virtual screening, 200 perspective agents were selected from more than 7 million compounds. After resulting molecular docking in CCDC GOLD, we specify 19 leading compounds. We propose these compounds as potent tubulin inhibitors and recommend them for in vitro testing as new fungicides. Conclusions. Based on results of high-throughput virtual screening in Grid, 19 new imidazole inhibitors of parasitic fungi tubulin were selected.Keywords: microtubule, tubulins, fungicides, imidazole derivatives, virtual screening, molecular docking.

InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening

2020 ◽  
Author(s):  
Taj Mohammad ◽  
Yash Mathur ◽  
Md Imtaiyaz Hassan
Keyword(s):  
Molecular Docking ◽  
User Interface ◽  
Virtual Screening ◽  
High Throughput ◽  
Graphical User Interface ◽  
High Throughput Screening ◽  
Computational Method ◽  
Recognition Mechanism ◽  
Ligand Interaction ◽  
High Throughput Virtual Screening

Abstract Exploring protein–ligand interactions is a subject of immense interest, as it provides deeper insights into molecular recognition, mechanism of interaction and subsequent functions. Predicting an accurate model for a protein–ligand interaction is a challenging task. Molecular docking is a computational method used for predicting the preferred orientation, binding conformations and the binding affinity of a ligand to a macromolecular target, especially protein. It has been applied in ‘virtual high-throughput screening’ of chemical libraries containing millions of compounds to find potential leads in drug design and discovery. Here, we have developed InstaDock, a free and open access Graphical User Interface (GUI) program that performs molecular docking and high-throughput virtual screening efficiently. InstaDock is a single-click GUI that uses QuickVina-W, a modified version of AutoDock Vina for docking calculations, made especially for the convenience of non-bioinformaticians and for people who are not experts in using computers. InstaDock facilitates onboard analysis of docking and visual results in just a single click. To sum up, InstaDock is the easiest and more interactive interface than ever existing GUIs for molecular docking and high-throughput virtual screening. InstaDock is freely available for academic and industrial research purposes via https://hassanlab.org/instadock.

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...

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...

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