Postgraduate Course: In Silico Drug Discovery (PGBI11079)

Course Outline
School	School of Biological Sciences	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)
Course type	Online Distance Learning	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
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: 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. Distinguish between force field/enthalpy based and knowledge based pose scoring methods. Understand the properties that increase the value of compound databases. Have a clear understanding of the values and problems associated with multiconformer vs single conformer chemical databases. Describe the major classes of similarity searching algorithms, and understand the types of problem that such programs can be applied to.