Učni načrt predmeta

Predmet:
Načrtovanje učinkovin na osnovi molekulskega in QSAR modeliranja
Course:
Drug Design Based on Molecular and QSAR Modelling
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Nanoznanosti in nanotehnologije, 3. stopnja / 1 1
Nanosciences and Nanotechnologies, 3rd cycle / 1 1
Vrsta predmeta / Course type
Izbirni
Univerzitetna koda predmeta / University course code:
NANO3-814
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
15 15 15 10 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Marjana Novič
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisits:
Vsebina:
Content (Syllabus outline):
Temeljna literatura in viri / Readings:
Cilji in kompetence:
Objectives and competences:
Predvideni študijski rezultati:
Intendeded learning outcomes:
Metode poučevanja in učenja:
Learning and teaching methods:
Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga
50 %
Seminar work
Ustni zagovor seminarske naloge
50 %
Oral defense of seminar work
Reference nosilca / Lecturer's references:
1. MORA LAGARES, Liadys, PEREZ CASTILLO, Yunierkis, MINOVSKI, Nikola, NOVIČ, Marjana. Structure%function relationships in the human P-Glycoprotein (ABCB1) : insights from molecular dynamics simulations. International journal of molecular sciences, 2022, vol. 23, str. 1-24
2. VENKO, Katja, NOVIČ, Marjana, STOKA, Veronika, ŽEROVNIK, Eva. Prediction of transmembrane regions, cholesterol and ganglioside binding sites in amyloid-forming proteins indicate potential for amyloid pore formation. Frontiers in molecular neuroscience, ISSN 1662-5099, 2021, vol. 14, str. 619496-1-619496-15
3. FJODOROVA, Natalja Stanislavovna, NOVIČ, Marjana, VENKO, Katja, RASULEV, Bakhtiyor. A comprehensive cheminformatics analysis of structural features affecting the binding activity of fullerene derivatives. Nanomaterials, ISSN 2079-4991. [Online ed.], 2020, vol. 10, str. 90-1-90-23
4. MORA LAGARES, Liadys, MINOVSKI, Nikola, NOVIČ, Marjana. Multiclass classifier for P-glycoprotein substrates, inhibitors, and non-active compounds. Molecules, ISSN 1420-3049, 2019, vol. 24, str. 2006-1-2006-22
5. DRGAN, Viktor, ŽUPERL, Špela, VRAČKO, Marjan, CAPPELLI, Claudia Ileana, NOVIČ, Marjana. CPANNatNIC software for counter-propagation neural network to assist in read-across. Journal of cheminformatics, 2017, vol. 9, 30. https://doi.org/10.1186/s13321-017-0218-y