27618 Chemoinformatics in drug discovery
Technological specialization course,
Bioinformatics and Systems Biology
Technological specialization course, Pharmaceutical Design and
Technological specialization course, Advanced and Applied
|Kemoinformatik i lægemiddelforskning|
Point( ECTS )
Taught under single-course student
|Technological specialization course, MSc. Eng., Advanced and Applied Chemistry|
|E5B (Wed 13-17)
THE COURSE IS NOT OFFERED IN AUTUMN 2016.
Scope and form:
|Lectures, computer exercises, mini-projects|
Duration of Course:
Type of assessment:
General course objectives:
The aim of the course is to introduce the participants to various
chemoinformatics methods and the theory behind them, to show
examples of the use of chemoinformatics in modern drug research,
and to give the participants practical experience through hands-on
A student who has met the objectives of the course will be able to:
- Define chemoinformatics and name the main areas of application
within drug discovery.
- Interpret the most important formats used for describing
- Describe the most widely used machine learning tools in
chemoinformatics and the algorithms that they are based on.
- Understand the differences between linear and non-linear
models, supervised and unsupervised machine-learning, clustering
- Argue on how to choose the appropriate computational tools for
a given problem.
- Plan, carry out and present computer exercises and
mini-projects as team work.
- Interpret the output from and evaluate the performance of a
given computational tool.
- Navigate and extract information from annotated chemical
- Construct and interpret drug-protein interaction networks.
- Create scientific posters and present projects orally.
- Apply peer-assessment on scientific report
Data sets: Extraction of data from a large database, evaluation of
Molecular structures: Graphical representation, 1D, 2D and 3D
molecular structures, pharmacophores.
Molecular descriptors: Generation of descriptors reflecting the
physical and chemical properties of the molecules. Molecular
Data analysis: Clustering, classification and regression methods.
Multi-linear regression. self-organizing maps, principal component
analysis, artificial neural networks, decision trees, support
Applications of chemoinformatics in drug research: Chemical
libraries, chemogenomics, virtual screening, docking,
protein-ligand interactions, network pharmacology, quantitative
structure-activity relationships (QSARs), and prediction of ADMET
properties (absorption, distribution, metabolism, elimination and
Tools: Internet-based programs, databases, in-house and commercial
Green challenge participation:
Please contact the teacher for information on whether this course
gives the student the opportunity to prepare a project that may
participate in DTU´s Study Conference on sustainability, climate
technology, and the environment (GRØN DYST). More infor
, Lyngby Campus, Building 208, Ph. (+45) 4525
2425 , email@example.com
|27 Department of
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Last updated: 11. juli, 2016