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Title:Napovedovanje lastnosti molekul z grafovskimi nevronskimi mrežami : magistrsko delo
Authors:ID Novak, Lovro (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Podgorelec, David (Comentor)
Files:.pdf MAG_Novak_Lovro_2025.pdf (3,17 MB)
MD5: 6DC0B0E973EF445BAAEC1A87D5F3CE4B
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Grafovske nevronske mreže (GNN) so se izkazale kot zmogljivo orodje za napovedovanje lastnosti struktur, predstavljenih v obliki grafov. Molekule lahko predstavimo kot grafe, kjer vozlišča predstavljajo atome, povezave pa kemijske vezi med njimi. V magistrski nalogi preučujemo učinkovitost GNN v kemoinformatiki za napovedovanje topnosti in temperature vrelišča molekul. Napovedovanje izvajamo z večopravilnim modelom ter z ločenimi, specializiranimi modeli za posamezno lastnost.
Keywords:grafovske nevronske mreže, graf, SMILES, napovedovanje lastnosti molekul
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[L. Novak]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (VIII, 48 str.))
PID:20.500.12556/DKUM-95729 New window
UDC:004.8.032.26(043.2)
COBISS.SI-ID:262461955 New window
Publication date in DKUM:16.12.2025
Views:186
Downloads:35
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:16.10.2025

Secondary language

Language:English
Title:Predicting molecular properties with graph neural networks
Abstract:Graph Neural Networks (GNN) have proven to be a powerful tool for predicting the properties of structures represented as graphs. Molecules can be represented as graphs, where nodes represent atoms and edges represent the chemical bonds between them. In this master’s thesis, we investigate the effectiveness of GNN in chemoinformatics for predicting molecular solubility and boiling point. The predictions are performed using a multitask model as well as separate, specialized models for each property.
Keywords:graph neural networks, graph, SMILES, prediction of molecular properties


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