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master's thesis
Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari
Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology, 2019. urn:nbn:hr:149:923359
Sindičić Đuretec, Valnea
University of Zagreb
Faculty of Chemical Engineering and Technology

Institutional repository: Repository of Faculty of Chemical Engineering and Technology University of Zagreb

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Sindičić Đuretec, V. (2019). Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari (Master's thesis). Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology. Retrieved from https://urn.nsk.hr/urn:nbn:hr:149:923359

Sindičić Đuretec, Valnea. "Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari." Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2019. https://urn.nsk.hr/urn:nbn:hr:149:923359

Sindičić Đuretec, Valnea. "Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari." Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2019. https://urn.nsk.hr/urn:nbn:hr:149:923359

Sindičić Đuretec, V. (2019). 'Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari', Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, accessed 20 December 2024, https://urn.nsk.hr/urn:nbn:hr:149:923359

Sindičić Đuretec V. Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari [Master's thesis]. Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology; 2019 [cited 2024 December 20] Available at: https://urn.nsk.hr/urn:nbn:hr:149:923359

V. Sindičić Đuretec, "Analiza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari", Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:149:923359

Metadata
TitleAnaliza molekulskih deskriptora u kvantitativnim odnosima strukture i topljivosti tvari
Title (english)Analysis of molecular descriptors in quantitative structure-solubility relationships
AuthorValnea Sindičić Đuretec
MentorŠime Ukić (mentor)
Committee memberŠime Ukić (predsjednik povjerenstva)
Committee memberMirjana Novak Stankov (član povjerenstva)
Committee memberMarijana Kraljić Roković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Chemical Engineering and Technology
Zagreb
Defense date and country2019-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Chemistry
Abstract
Upotreba kvantitativnih odnosa strukture i svojstava tvari (QSPR) jedna je od glavnih ekonomskih alternativa u eksperimentalnom testiranju molekulskih svojstava, kao što je topljivost tvari u vodi, log S. U ovom radu, izrađeni su QSPR modeli primjenjujući algoritme strojnog učenja, stabala odluke i nasumičnih šuma, koristeći programski jezik Python. Modeli su rađeni na setu od 1311 molekula; koristili su se različiti molekulski deskriptori i molekulski otisci. Usporedbom modela pokazalo se kako algoritam nasumičnih šuma ima bolju moć predviđanja od algoritma stabla odluke. Također se pokazalo da modeli koji koriste molekulske deskriptore imaju bolju moć predviđanja od onih koji koriste molekulske otiske. Kao najbolji izabran je model nasumičnih šuma uz uporabu molekulskih deskriptora. Razvijeni model mogao bi poslužiti za predviđanje topljivosti molekula kandidata prilikom razvoja novih lijekova kako bi se smanjili troškovi eksperimentalnih ispitivanja.
Abstract (english)
The use of quantitative structure-property relationship (QSPR) is one of the major economic alternatives in experimental testing of molecular properties, such as the aqueous solubility of a substance, log S. In this master thesis, QSPR models were developed using machine learning algorithms, decision trees and random forests. The modeling was performed using Python programming language. The models were developed on a set of 1311 molecules; different molecular descriptors and molecular fingerprints were used. Model comparisons showed that the random forest algorithm had better prediction power than the decision tree one. Models using molecular descriptors indicated better prediction power than those using molecular fingerprints. The model of random forests using molecular descriptors was the one with the best characteristics. Once developed, the QSPR solubility model could be applied in developing process of new drugs in order to reduce the cost of experimental trials.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:149:923359
ProjectNumber: IP-2014-09-7992
Title: Modeliranje okolišnih aspekata napredne obrade voda za razgradnju prioritetnih onečišćivala
Title: Modeling of environmental aspects of advanced water treatment for fegradation of priority pollutants
Acronym: MEAoWT
Leader: Tomislav Bolanča
Jurisdiction: Croatia
Funder: HRZZ
Funding stream: IP
Study programmeTitle: Applied Chemistry - Graduate study; specializations in: Environmental chemistry and green technologies, Advanced materials and technologies, Applied organic chemistry
Course: Applied organic chemistry
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra primijenjene kemije (magistar/magistra primijenjene kemije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
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Created on2022-02-27 15:07:02