BPS3031 · Computational drug design

Overview

This unit introduces you to the key concepts and practical application of computational methods in chemistry and drug discovery. The unit will teach fundamental programming skills using the widely-used programming language Python and apply them to the key skills of as data visualization, chemoinformatics, and machine learning. It will cover important molecular modelling methods including molecular docking, molecular dynamics, and quantum mechanical calculations, as well as bioinformatics methods. You will learn to use molecular modelling software and to construct and validate QSAR models, use supervised and unsupervised learning techniques, and to critically evaluate the role of computational tools in drug development.

Offerings

Assessment

The Handbook lists an examination for this unit.

In-semester assessment 50%, final assessment 50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Apply fundamental programming skills using the programming language Python, including using use of variables, conditionals, loops, functions and data visualization
2. Utilise molecular modelling techniques including molecular docking, molecular dynamics, and basic quantum mechanical calculations using computational chemistry software
3. Use QSAR and chemoinformatics techniques to analyse molecular and biological data.
4. Build, validate, and interpret statistical and machine learning models for regression and classification in chemistry
5. Perform bioinformatics tasks such as sequence alignment and BLAST searches
6. Critically analyse the use of computational methods in drug development

Workload

• Twelve 1-hour online modules (discovery) • Twenty-four 1-hour interactive lectures (online modules) • Six 2-hour Q&A sessions • Ten 3-hour workshops • One hour of scheduled assessment

Student experience, not official rules. Nothing here changes what the Handbook says.