TOPICS IN COMPUTER MODELLING - 2023/4

Module Overview

This module is intended to present advanced molecular modelling (building on previous modeling experience in CHE1040 and CHE2043) to give students experience with modern computer aided drug design. We will cover most aspects of modern computational drug design from a practical viewpoint, where students learn by doing and not by just listening. If you are doing a computational final year project in CHE3047 or CHEM029 this module will give you the skills needed. 

Module provider

Chemistry and Chemical Engineering

Module Leader

HOWLIN Brendan (Chst Chm Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

Module content

Indicative content includes:

 

• 
  Introduction to bioinformatics
  


• 
  Internet resources for bioinformatics
  


• 
  Examining protein structure
  


• 
  Using MOE (Molecular Operating Environment) for molecular modelling of proteins
  


• 
  Protein-ligand docking in pharmaceutical drug design
  


• 
  Examples of protein modelling using pharmaceutical examples e.g. GPCRs (G protein coupled receptors) and plant proteins
  


• 
  Further analysis of protein structure using molecular dynamics
  


• 
  Point mutation and mutational analysis
  


• 
  Examples of applications to current research
  


• 
  Introduction to quantum chemical calculations: Schrödinger equation, Born-Oppenheimer approximation, Hartree-Fock method, DFT (Denisty Functional Theory) method
  


• 
  Wave functions, basis sets
  


• 
  Closed shell and open shell systems, self-consistent field
  


• 
  Optimisation techniques, gradients, Hessian matrix
  


• 
  Post-Hartree-Fock concepts: many-body perturbation theory, configuration interaction. QM/MM methods, density functional theory
  


• 
  Use of ab initio program package GAUSSIAN. Structure of input and output files
  


• 
  Interpretation of the results of ab initio calculations: geometries and energies
  


• 
  Interpretation of the results of ab initio calculations: molecular properties
  


• 
  Interpretation of the results of ab initio calculations: molecular orbitals
  

 

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

Why are we doing this? 

The assessment strategy is designed to allow students to develop and test their knowledge and their skills in a manner that not only enhances their understanding of the topic, but also allows them to situate it within the wider context of the subject area, thereby contributing to the coherency of their learning journey. The module therefore builds upon learning (and feedback) acquired in previous modules and the assessments contain valuable employability components and test a range of transferable skills. The assessment strategy also allows for assessment to take place in a supportive context through a practical exam. Such an approach contributes to the development of students as independent learners by empowering them to self-evaluate, and reflect on, their own performance in relation to others. Other elements of the assessment strategy allow students to test their performance in relation to ‘real-life’ modelling exercises. The combination of a practical exam to test their digital skills and a written exam to test their knowledge of theory allows students to integrate the two halves of the course. This will allow students to understand, evaluate, and critically examine the use and application of different techniques by professional modelers, and to reflect on their learning from previous modules in relation to graphics, modelling, and interpretation. All aspects of the assessment strategy allow students to receive feedback from expert staff. 

The assessment strategy is designed to allow students to demonstrate  

Practical skills in Molecular Modelling and knowledge of the underlying theory. 

Thus, the summative assessment for this module consists of: 

• 
  Practical on-line examination, (meets learning outcomes 1,2,4) 
  


• 
  Online open book examination,¿(meets learning outcomes 3 and 5) 
  

Formative assessment 

A 'mock' practical exam will be held, and the results discussed 

 Feedback 

Informal formative assessment is conducted throughout the module during the weekly classes and students receive full feedback on a mock practical exam and the academic will go through a previous theory exam during class and give full feedback. Feedback and feedforward on summative assignments will be provided via Surrey Learn. This will indicate what students did well, less well, and what they need to do to improve in the future and will relate both to issues specific to the module and to transferable skills. Formative feedback will be provided throughout the module within in-class discussions and activities, and tutorials  

Module aims

• To discuss the theory and practice of modelling as applied to pharmaceuticals and proteins.
• To provide the background necessary for students to comprehend and criticise the results of simulation on the above systems.
• To give students the opportunity to carry out and comment on the results of a simulation
• To cover a range of selected topics in molecular orbital calculations appropriate to research.
• To cover a range of selected topics in chemometrics appropriate to research.
• To introduce students to advanced techniques of molecular modelling applied to both small molecules and proteins, and the use of a range of databases in these studies.

Learning outcomes

Methods of Teaching / Learning

  The Indicated Lecture Hours (which may also include seminars, tutorials, workshops, and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organized separately to taught content and will be published on to student personal timetables, where they apply to taken modules as soon as they are finalized by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester he learning and teaching strategy is designed to: 

 

Give the student both practical and theoretical knowledge of modern molecular modelling. 

 

The¿learning and teaching¿methods include: 
 
A hands-on workshop approach will be taken to computational modelling in the computing laboratory where students will work at their own pace with guidance from the academic. Initially we will reproduce some results from a published paper and critically evaluate the results as a group (resourcefulness and resilience) and then we will computationally modify the ligands in the paper with the highest binding energy to increase their binding energy and hence efficacy (Digital Skills). Necessary theory will be delivered alongside the practical work to integrate it into the module. 

. 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: CHE3053

Other information

The School of Chemistry and Chemical Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed

to allow students to develop knowledge, skills, and capabilities in the following areas: The module concentrates on using computers to run state of the art graphics and molecular orbital software so will enhance the Digital capabilities of students, As we start with a worked example which the student shave to reproduce, this problem-solving approach will increase the Resourcefulness and resilience of students, both digital capability and resourcefulness are the skills looked for in employment in the pharmaceutical industry and particularly attractive to employers hence enhancing the Employability of students.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.