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 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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
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.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | InSilico |
Contacts
| Course organiser | Dr Douglas Houston
Tel: (0131 6)50 7358
Email: DouglasR.Houston@ed.ac.uk |
Course secretary | Mrs Claire Black
Tel: (0131 6)50 8637
Email: Claire.Black@ed.ac.uk |
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