MPŠ
MPŠ MP&Scaron MP&Scaron MP&Scaron Avtorji

Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

Search

Course Description

Drug Design Based on Molecular and QSAR Modelling

Program

Nanosciences and Nanotechnologies, third-level study programme

Lecturers:

prof. dr. Marjana Novič

Goals:

Getting acquainted with the computer methods to model the properties of molecules. The properties that are of interest in drug design and in chemical regulations - risk assessment of compounds in the environment will be addressed.
- Collecting knowledge about the databases that contain information about the structures and properties of compounds, and about the methods for data handling.
- Learning about the methods for encoding of molecular structures and for calculating molecular descriptors.
- Acquiring the knowledge about the statistical modeling and validation of models.
- Acquiring basic knowledge about molecular modelling – virtual scrambling, docking.

Content:

Presentation of data banks, which are accessible via the Internet, and possibly compilation of students' own data banks for the different biological properties (dose and grades of toxicity, teratogenicity, carcinogenicity, binding constants of certain enzymes, etc.).
- Encoding of chemical structures (SMILES notation, MDL, SDF, MOL).
- Calculation of descriptors (topological, empirical, quantum-chemical, etc.) and use of relevant computer programs (DRAGON, CODESSA, etc.).
- Use various software packages for the construction and validation of QSPR models (linear regression, principal component analysis, neural nets, etc.).

- Modeling (linear and nonlinear) In this chapter, students will learn the basics of multiple linear regression (MLR) as an example of the linear regression. As example of the non-linear techniques various artificial neural networks will be presented.
- Transformation of the measurement space Some common measurement space transformation will be presented (e.g. PCA, etc.), which are used to enable the graphical presentation of the multi-dimensional metric space.
- Clustering
We will present a simple procedure for clustering of data in a multidimensional space of measurements, as well as the use of artificial neural networks for the same purpose.
- Model validation We will learn the basic procedures for dividing data in different sets needed for model validation (learning and testing set). We will also discuss the methods used in various model validations.
- Mathematical representation of chemical structures. Some simple representations of chemical structures that can be used in modeling the relationship between chemical structure and properties of molecules (QSAR, QSPR) will be discussed.
- Practical application of the knowledge obtained – a case study of design of inhibitors of a selected enzyme.

Course literature:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:

1. D.L Massart et al., Handbook of Chemometrics and Qualimetrics, Elsevier, Amsterdam, 1997. Part B, pp. 383-417
2. J. Zupan, J. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, VCH, Weinheim, 1999. pp. 125-358
Additional literature (Selected chapters)
3. L. Hansch, A. Leo, Exploring QSAR Fundamentals and Applications in Chemistry and Biology, American Chemical Society, Washington, DC 1995.
4. MINOVSKI, Nikola, NOVIČ, Marjana. Integrated in silico methods for the design and optimization of novel drug candidates : a case study on fluoroquinolones - Mycobacterium tuberculosis DNA gyrase inhibitors. V: ROY, Kunal (ur.). Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment, (Advances in chemical and materials engineering book series (Print), ISSN 2327-5448). Harsley: Medical Information Science Reference (an imprint of IGI Global), cop. 2015, str. 269-317.

Additional literature available for the individual seminars in case of the tutoring approach.

Significant publications and references:

Book chapters
1. NOVIČ, Marjana. Kohonen and counter-propagation neural networks applied for mapping and interpretation of IR spectra. V: LIVINGSTONE, David (ur.). Artificial neural networks : methods and applications. Humana Press, 2007, str. [1-15].
2. MINOVSKI, Nikola, NOVIČ, Marjana. Integrated in silico methods for the design and optimization of novel drug candidates : a case study on fluoroquinolones - Mycobacterium tuberculosis DNA gyrase inhibitors. V: ROY, Kunal (ur.). Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment, (Advances in chemical and materials engineering book series (Print), ISSN 2327-5448). Harsley: Medical Information Science Reference (an imprint of IGI Global), cop. 2015, str. 269-317
Scientific papers
1. LETONDOR, Christophe, PORDEA, Anca, HUMBERT, Nicolas, IVANOVA, Anita, MAZUREK, Sylwester, NOVIČ, Marjana, WARD, Thomas R. Artificial transfer hydrogenases based on the biotin-(strept)avidin technology : fine tuning the selectivity by saturation mutagenesis of the host protein. J. Am. Chem. Soc., 2006, vol. 128, no. 25, str. 8320 -8328. [COBISS.SI-ID 3516442]
2. ŽUPERL, Špela, PRISTOVŠEK, Primož, MENART, Viktor, GABERC-POREKAR, Vladka, NOVIČ, Marjana. Chemometric approach in quantification of structural identity/similarity of proteins in biopharmaceuticals. J. chem. inf. mod., 2007, vol. 47, no. 3, str. 737-743. [COBISS.SI-ID 3725082]
3. ROY CHOUDHURY, Amrita, NOVIČ, Marjana. Data-driven model for the prediction of protein transmembrane regions. SAR and QSAR in environmental research, ISSN 1062-936X, 2009, vol. 20, no. 7/8, str. 741-754.
4. ROY CHOUDHURY, Amrita, PERDIH, Andrej, ŽUPERL, Špela, SIKORSKA, Emilia, ŠOLMAJER, Tomaž, JURGA, Stefan, ZHUKOV, Igor, NOVIČ, Marjana. Structural elucidation of transmembrane transporter protein bilitranslocase : conformational analysis of the second transmembrane region TM2 by molecular dynamics and NMR spectroscopy. Biochimica et biophysica acta, Biomembranes, ISSN 0005-2736. [Print ed.], 2013, vol. 1828, iss. 11, str. 2609-2619.
5. MARTINČIČ, Rok, KUZMANOVSKI, Igor, WAGNER, Alain, NOVIČ, Marjana. Development of models for prediction of the antioxidant activity of derivatives of natural compounds. Analytica chimica acta, ISSN 0003-2670. [Print ed.], Apr. 2015, vol. 868, str. 23-35.

Examination:

Seminar work (50%)
Oral defense of seminar work (50%)

Students obligations:

Seminar work.
Oral defense of seminar work.

Links: