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Vistoli, G.* ; Manelfi, C.* ; Talarico, C.* ; Fava, A.* ; Warshel, A.* ; Tetko, I.V. ; Apostolov, R.* ; Ye, Y.* ; Latini, C.* ; Ficarelli, F.* ; Palermo, G.* ; Gadioli, D.* ; Vitali, E.* ; Varriale, G.* ; Pisapia, V.* ; Scaturro, M.* ; Coletti, S.* ; Gregori, D.* ; Gruffat, D.* ; Leija, E.* ; Hessenauer, S.* ; Delbianco, A.* ; Allegretti, M.* ; Beccari, A.R.*

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions.

Expert Opin. Drug Discov. 18, 821-833 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
INTRODUCTION: Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community. AREAS COVERED: In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds. EXPERT OPINION: Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Collaborative Computing ; Sars-cov-2 ; Artificial Intelligence ; Docking Simulations ; Drug Repurposing ; Virtual Screening; Drug Discovery; Prediction
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1746-0441
e-ISSN 1746-045X
Zeitschrift Expert Opinion on Drug Discovery
Quellenangaben Band: 18, Heft: 8, Seiten: 821-833 Artikelnummer: , Supplement: ,
Verlag Informa Healthcare
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503000-001
Förderungen EU
DOI 10.1080/17460441.2023.2221025
WOS ID 001026801600001
Scopus ID 85164722943
PubMed ID 37424369
Erfassungsdatum 2023-10-06
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