DM840: Algorithms in Cheminformatics

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340003112, N340003102
Censorship: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn, Spring
Level: Master's level course approved as PhD course

STADS ID (UVA): N340003101
ECTS value: 10

Date of Approval: 27-03-2019


Duration: 1 semester

Version: Approved - active

Comment

15018101 (former UVA) is identical with this course description. 

Entry requirements

None

Academic preconditions

Students taking the course are expected to:

  • be able design and implement programs, using standard algorithmic approaches and data structures
  • be able to judge the complexity of algorithms, with regard to runtime as well as with regard to space usage.

Course introduction

The purpose of this course is to enable the student to solve a wide range of non-trivial discrete computational problems within computer science by applying advanced algorithmic ideas, graph theoretical approaches, knowledge from related fields of discrete mathematics, and complexity theory to problems motivated from or arising in chemistry. The course gives an academic basis for writing a Master's thesis, that aims to apply core Computer Science approaches to relevant questions in Chemistry, Biology, Physics, or Mathematics.

In relation to the competence profile of the degree it is the explicit focus of the course to:

  • Provide knowledge on a range of specialized models and methods developed in computer science based on the highest international research standards, including topics from the subject's research front
  • Give knowledge of computer science models and methods for use in other professional areas
  • Describe, analyse, and solve advanced computer scientific problems using the models they learned.
  • Shed light on stated hypotheses with a qualified theoretical basis and be critical of both own and others research results and scientific models.
  • Develop new variants of the learned methods, where the concrete problem requires it.
  • Disseminate research-based knowledge and discuss professional and scientific problems with both colleagues and non-specialists.
  • Plan and execute scientific projects of high standard, including managing work situations that are complex, unpredictable, and require novel solutions.
  • Take responsibility of own professional development and specialisation have learned.
  • Be able to launch and implement scientific and interdisciplinary cooperation and take professional responsibility

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:
  • apply and explain methods, models, and algorithmic ideas covered in the course,
  • formulate the above in precise language and notation,
  • implement algorithms and data structures from the course,
  • describe the implementation and experimental work done in clear and precise language, and in a structured fashion.

Content

The following main topics are contained in the course:
  • Representation of Molecular Structures
  • Graph Isomorphism and Graph Canonicalization
  • Combinatorial Structures
  • Pólya's Counting Theory
  • Artificial Chemistries
  • Metabolic Networks and Metabolic Pathways
  • Flux Balance Analysis
  • Organization Theory
  • Petrinets
  • Graph Transformation
  • Analytic Combinatorics
  • Concurrency Theory for Cheminformatics
  • Applications of Algorithmic Cheminformatics in Industry

Literature

See Blackboard for syllabus lists and additional literature references.

Examination regulations

Prerequisites for participating in the exam a)

Timing

Autumn and Spring

Tests

Mandatory assignments

EKA

N340003112

Censorship

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

0

Additional information

The prerequisite examination is a prerequisite for participation in exam element a)

Exam element a)

Timing

January and June

Prerequisites

Type Prerequisite name Prerequisite course
Examination part Prerequisites for participating in the exam a) N340003101, DM840: Algorithms in Cheminformatics

Tests

Oral exam

EKA

N340003102

Censorship

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

10

Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

70 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

In the intro phase, concepts, theories and models are introduced and put into perspective. In the training phase, students train their skills through exercises and dig deeper into the subject matter. In the study phase, students gain academic, personal and social experiences that consolidate and further develop their scientific proficiency. Focus is on immersion, understanding, and development of collaborative skills.

Activitys during the study phase:
  • Using the acquired knowledge in projects.
  • Discussing the scientific articles/book chapters

Teacher responsible

Name E-mail Department
Daniel Merkle daniel@imada.sdu.dk

Timetable

49
Monday
30-11-2020
Tuesday
01-12-2020
Wednesday
02-12-2020
Thursday
03-12-2020
Friday
04-12-2020
08 - 09
Class h1e
Undervisning
09 - 10
Class h1e
Undervisning
10 - 11
11 - 12
12 - 13
Class f
Forelæsning
13 - 14
Class f
Forelæsning
14 - 15
15 - 16
16 - 17
Class f
Forelæsning
17 - 18
Class f
Forelæsning
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Administrative Unit

Institut for Matematik og Datalogi (datalogi, fiktiv)

Team at Registration & Legality

NAT

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