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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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DRPS : Course Catalogue : School of Biological Sciences : Postgraduate

Postgraduate Course: In Silico Drug Discovery (PGBI11079)

Course Outline
SchoolSchool of Biological Sciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
Summary**Online Distance Learning Course**

A major contributor of new leads to the drug discovery process is the large scale, normally database driven, modelling of ligand/macromolecular interactions or searches based on properties derived from pre-existing chemical entities. In this course these database mining techniques, along with allied chemical fragment based approaches, will be explored.
Course description Not entered
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2017/18, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Course Start Date 09/04/2018
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Online Activities 20, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 50 % group assessment (including maximum 20% variation for Peer Assessment of contribution)

50 % electronic portfolio comprising learning log and contribution to Skills Profile
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. At the end of this course students should be able to: Describe the major aspects of a virtual screening application, including the methods for initial site point generation, and the various approaches to pose fitting.
  2. Distinguish between force field/enthalpy based and knowledge based pose scoring methods.
  3. Understand the properties that increase the value of compound databases.
  4. Have a clear understanding of the values and problems associated with multiconformer vs single conformer chemical databases.
  5. Describe the major classes of similarity searching algorithms, and understand the types of problem that such programs can be applied to.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsInSilico
Contacts
Course organiserDr Douglas Houston
Tel: (0131 6)50 7358
Email:
Course secretaryMrs Claire Black
Tel: (0131 6)50 8637
Email:
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