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

Institutional repository: Repository of the Faculty of Science

Cite this document

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

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

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

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 15 May 2024, https://urn.nsk.hr/urn:nbn:hr:217:136071

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 2024 May 15] Available at: https://urn.nsk.hr/urn:nbn:hr:217:136071

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

Metadata
TitleMultiple 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
AuthorFilip Sente
MentorTomica Hrenar (mentor)
MentorKristian Vlahoviček (komentor)
Committee memberTomica Hrenar (predsjednik povjerenstva)
Committee memberKristian Vlahoviček (član povjerenstva)
Committee memberDubravka Hranilović (član povjerenstva)
Committee memberBiljana Balen (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Biology)
Zagreb
Defense date and country2024-02-27, 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)
Languageenglish
URN:NBNurn:nbn:hr:217:136071
ProjectNumber: 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 programmeTitle: 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 resourceText
File originBorn digital
Access conditionsEmbargoed access
Embargo expiration date: 2026-02-27
Terms of useLicence In copyright icon
Created on2024-03-19 11:34:01