TOPICS IN COMPUTER MODELLING - 2025/6

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 computational chemistry and data science applied to chemistry research. We will cover most aspects of modern computational chemistry 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

SACCHI Marco (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:Quantum mechanics:An introduction to the principles that govern and affectenergy calculations for small molecules. Follows on fromCHE1040 and CHE2043 content on molecular modelling. Includes ahands-on introduction to Density Functional Theory.Molecular dynamics:Use of appropriate software and algorithms to simulatemolecular motion and dynamic systems in Chemistry andChemical Engineering.Protein docking:Principles of chemoinformatics, and their applications tothe design of drugs through screening. Use of dockingsoftware to understand the binding of a set of potentialdrug molecules.Scientific programming:A first introduction to programming language (Python,MATLAB) and their application to chemical research invisualisation of data and automation of simple tasks. 

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, Data Science and Programming and knowledge of the underlying theory. Thus, the summative assessment for this module consists of: 

• Coursework on Computational Chemistry (LOs 1, 3 and 4)
• Coursework on Data Analysis and Programming (LOs 1 and 2)

 Feedback Informal formative assessment is conducted throughout the module during the weekly classes. 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 a variety of systems, from small molecules to proteins.
• To provide the background necessary for students to comprehend and criticise the results of simulation on the above systems.
• To introduce students to advanced techniques of data analysis and programming for data science.
• To cover a range of selected topics in molecular orbital calculations and molecular dynamics appropriate to research.
• To cover a range of selected topics in computer modelling and data analysis as 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

Indicated Lecture Hours (which may also includeseminars, tutorials, workshops, and other contact time)are approximate and may include in-class tests whereone or more of these are an assessment on the module.In-class tests are scheduled/organized separately totaught content and will be published on to studentpersonal timetables, where they apply to taken modulesas soon as they are finalized by central administration.This will usually be after the initial publication of theteaching timetable for the relevant semester. The learningand teaching strategy for this module is designed to:Provide students with the opportunity to demonstratepractical skills in computer modelling by using modernsoftware in a computer laboratory to solve relevantchemical problems with support from the academic anddemonstrator, Students are encouraged to go beyond thebrief and develop their own theories and ideas, with thehelp of wider reading. Students with these skills are highlyemployable in the pharmaceutical industry, for example. 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. 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 practical capabilities in computational chemistry and data science. We will use a problem-solving approach to increase the resourcefulness and resilience of students. Both digital capability and resourcefulness are the skills looked for in employment in the pharmaceutical and chemical 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 2025/6 academic year.