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eNauka >  Results >  Antimicrobial Rhenium Tricarbonyl Complexes: Accelerating their Discovery by Leveraging Machine Learning Models
Title: Antimicrobial Rhenium Tricarbonyl Complexes: Accelerating their Discovery by Leveraging Machine Learning Models
Authors: Nedyalkova, Miroslava; Demirci, Gozde; Cortat, Youri; Schindler, Kevin; Rhamani, Fatlinda; Horner, Justine; Crochet, Aurelien; Pavić, Aleksandar  ; Mamula Steiner, Olimpia; Zobi, Fabio;
Other contributors: Morić, Ivana  ; Đorđević, Valentina  
Issue Date: 2024
Publication: 5th Belgrade Bioinformatics Conference
Publisher: Belgrade : Institute of Molecular Genetics and Genetic Engineering
Type: Conference Paper
ISBN: 978-86-82679-16-5 Search Idenfier
Collation: str. 17-17
URI: https://imagine.imgge.bg.ac.rs/handle/123456789/2452
https://enauka.gov.rs/handle/123456789/928078
Project: M.L. and M.N. acknowledge financial support from the Swiss National Science Foundation through the NCCR Bio-inspired materials. M.L., F.Z., G.D., Y.C., A.C. acknowledge financial support from the University of Fribourg. K.S. and F.Z. acknowledge financial support from the Swiss National Science Foundation grant number 200021_196967. O.M.S. and J.H. acknowledge financial support from the Haute Ecole Spécialisée de Suisse Occidentale.
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