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27828 Chemoinformatics in drug discovery

Danish title:

Kemoinformatik i lægemiddelforskning


Point( ECTS )


Course type:

Taught under single-course student


E5B (Wed 13-17)


Campus Lyngby

Scope and form:

Lectures, computer exercises, mini-projects

Duration of Course:

13 weeks

Date of examination:


Type of assessment:



Recommended prerequisites:

General course objectives:

The aim of the course is to introduce the participants to various chemoinformatics methods, to show examples of the use of chemoinformatics in modern drug research, and to give the participants practical experience through hands-on chemoinformatics exercises.

Learning objectives:

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 molecular structures.
  • 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 and classification.
  • Argue on how to choose the appropriate computational tools for a given problem.
  • Describe rational work flows for data mining and for preparing high quality data sets for modeling purposes.
  • Interpret the output from and evaluate the performance of a given computational tool.
  • Navigate and extract information from annotated chemical libraries.
  • Construct and interpret drug-protein interaction networks.
  • Plan, carry out and present computer exercises and mini-projects as team work.
  • Be able to evaluate your own work and relevant scientific articles.
  • Present projects orally by using MS PowerPoint and by creating a scientific poster.


Data sets: Extraction of data from a large database, evaluation of structural diversity.
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 fingerprints.
Properties: Calculation of physical chemical properties such as solubility and partition coefficients, pharmacological properties such as absorption and toxicity, and global properties like oral bioavailability and drug-likeness.
Data analysis: Clustering, classification and regression methods. Multi-linear regression. self-organizing maps, principal component analysis, artificial neural networks, decision trees, support vector machines.
Applications of chemoinformatics in drug research: Chemical libraries, chemogenomics libraries, virtual screening, protein-ligand interactions and interaction networks, ligand activity profiling, quantitative structure-activity relationships (QSARs), and prediction of ADMET properties (absorption, distribution, metabolism, elimination and toxicity).
Tools: Internet-based programs, databases, in-house and commercial programs.


The individual, oral exam is based on the poster of the final mini-project but will also cover other parts of the curriculum.

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 http://www.groendyst.dtu.dk/english


27 Department of Systems Biology

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Last updated: 04. august, 2015