Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning

Sente, Filip

prikaz prve stranice dokumenta Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning

Embargo period 2026-02-27

master's thesis

Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning

Zagreb: University of Zagreb, Faculty of Science, 2024. urn:nbn:hr:217:136071

Sente, Filip

University of Zagreb
Faculty of Science
Department of Biology

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APA 6th Edition

Sente, F. (2024). Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:136071

MLA 8th Edition

Sente, Filip. "Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:136071

Chicago 17th Edition

Harvard

Sente, F. (2024). 'Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning', Master's thesis, University of Zagreb, Faculty of Science, accessed 16 January 2025, https://urn.nsk.hr/urn:nbn:hr:217:136071

Vancouver

Sente F. Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2024 [cited 2025 January 16] Available at: https://urn.nsk.hr/urn:nbn:hr:217:136071

IEEE

F. Sente, "Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:217:136071

Cite this item: https://urn.nsk.hr/urn:nbn:hr:217:136071

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Title	Multiple simultaneous docking of small molecules in butyrylcholinesterase active site using deep reinforcement learning
Title (croatian)	Višestruko istovremeno dokiranje malih molekula u aktivno mjesto butirilkolinesteraze primjenom dubokog pojačanog učenja
Author	Filip Sente
Mentor	Tomica Hrenar (mentor)
Mentor	Kristian Vlahoviček (komentor)
Committee member	Tomica Hrenar (predsjednik povjerenstva)
Committee member	Kristian Vlahoviček (član povjerenstva)
Committee member	Dubravka Hranilović (član povjerenstva)
Committee member	Biljana Balen (član povjerenstva)
Granter	University of Zagreb Faculty of Science (Department of Biology) Zagreb
Defense date and country	2024-02-27, Croatia
Scientific / art field, discipline and subdiscipline	NATURAL SCIENCES Biology
Abstract	Cholinesterase inhibitors are often used in treating Alzheimer’s disease. Molecular docking represents an important tool in the design of novel inhibitors. In this study, a multicomponent quantum chemical Monte Carlo semi flexible molecular docking of four selected components of the Ugi reaction was performed inside the active site of butyrylcholinesterase (BChE). The IC 50 value of compounds was evaluated, and artificial neural networks (ANNs) were trained by deep reinforcement learning for two of them, using results from docking. Even though components alone do not inhibit BChE significantly, formaldehyde and benzylamine can be used jointly for the design of novel drugs because they bind closely to the enzyme’s catalytic triad. In addition, the advantage of using the three component Ugi reaction is highlighted. Finally, ANN was shown to be successful in representing a potential energy surface of docking simulations. O btained ANN can be used as a scoring function for future dockings, with significantly shorter calculation time.
Abstract (croatian)	U liječenju Alzheimerove bolesti često se koriste inhibitori kolinesteraza. Molekularno dokiranje važan je alat u dizajnu novih inhibitora, a u ovome je radu unutar aktivnog mjesta butirilkolinesteraze (BChE) provedeno multikomponentno kvantno-kemijsko Monte Carlo polufleksibilno dokiranje četiriju komponenata Ugijeve reakcije. Određene su im i vrijednosti IC50 te su za dvije od njih istrenirane umjetne neuronske mreže (ANN) primjenom dubokog pojačanog učenja za opis rezultata dokiranja. Iako zasebne molekule ne inhibiraju značajno BChE, formaldehid i benzilamin zajedno mogu poslužiti u dizajnu novih lijekova jer se vežu u blizini katalitičke trijade enzima. Uz to, istaknuta je prednost uporabe trokomponentne Ugijeve reakcije. Naposlijetku, pokazano je da se ANN može uspješno koristiti u reprezentaciji plohe potencijalne energije dokiranja. Dobivena mreža može ubuduće poslužiti kao funkcija bodovanja, uz znatno kraće trajanje proračuna.
Keywords
Keywords (croatian)
Language	english
URN:NBN	urn:nbn:hr:217:136071
Project	Number: IP-2022-10-9525 Title: Metom usmjerena sinteza inhibitora kolinesteraza podržana strojnim učenjem Acronym: TargetCh Leader: Ines Primožič Jurisdiction: Croatia Funder: HRZZ Funding stream: IP-2022-10
Study programme	Title: Molecular Biology-University graduate programme Study programme type: university Study level: graduate Academic / professional title: sveučilišni/a magistar/magistra molekularne biologije (sveučilišni/a magistar/magistra molekularne biologije)
Type of resource	Text
File origin	Born digital
Access conditions	Embargoed access Embargo expiration date: 2026-02-27
Terms of use
Repository	Repository of the Faculty of Science
Created on	2024-03-19 11:34:01