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undergraduate thesis
Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:356717
Pešić, Katarina
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Pešić, K. (2024). Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:356717

Pešić, Katarina. "Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:356717

Pešić, Katarina. "Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:356717

Pešić, K. (2024). 'Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 04 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:356717

Pešić K. Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2025 January 04] Available at: https://urn.nsk.hr/urn:nbn:hr:168:356717

K. Pešić, "Predikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:356717

Metadata
TitlePredikcija topivosti u vodi kemijskih spojeva korištenjem kvantitativnih modela strukture i aktivnosti
Title (english)Prediction of water solubility of chemical compounds using quantitative structure-activity models
AuthorKatarina Pešić
MentorMihaela Vranić (mentor)
Committee memberMihaela Vranić (predsjednik povjerenstva)
Committee memberDamir Pintar (član povjerenstva)
Committee memberLuka Humski (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-02, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Uloga kemoinformatike u znanosti i industriji ne može se zanemariti. Kvalitetna analiza ponašanja molekula ključan je korak u, primjerice, farmaciji. Ocjena dobrote molekule može se donijeti na osnovu i njezine topivosti u vodi, jer ona direktno utječe na njezino ponašanje u tijelu i u okolišu. Pokazuje se da se topivost vode može dobro modelirati linearnim i nelinearnim kombinacijama karakteristika molekula, zvanih deskriptori, a posebno na novo i modelima strojnog učenja. U ovom radu korištena su tri modela na tri različita skupa podataka i pokazuje se da je moguće, kroz ispravnu provedbu, nekim modelima strojnog učenja kvalitetno predvidjeti topivosti molekula što je bitan pokazatelj toga da strojno učenje ima veliku ulogu i može ostati relevantno u području kemoinformatike.
Abstract (english)
The role of chemoinformatics in various fields of industry and science cannot be overlooked. Analysis of molecular behavior is a key step in, e.g., pharmacy. Goodness of fit of one particular molecule can be described through its water solubility, as it directly affects its behavior in the human body, as well as in the environment. It can be shown that water solubility may be well modeled through linear and non-linear combinations of other molecule properties, also called molecular descriptors, especially when paired with machine learning. In this thesis three distinct machine learning models were trained on three distinct data sets and it was shown that, when operated in the right way, it is possible to achieve trustworthy predictions of water solubility with machine learning. That serves as a confirmation of the role machine learning is taking in chemoinformatics, and why it is on a path to stay relevant in the field for the time to come.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:356717
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 21:50:04