The aim of this module is to teach the basics of computer programming and the use computational techniques for data storage and analysis. Methods for processing and extracting useful information from multivariate data sets for both exploratory analysis and supervised learning will be introduced. Pattern recognition and clustering algorithms also underpin rational approaches to structure-based drug design and techniques used to describe and compare the properties of small molecules will be covered.
At the end of this module students will be aware of available techniques, software and web-resources for chemoinformatics.
Lectures will provide an introduction to the elements of programming, algorithm construction and implementation. The R programming language will be used but the concepts are transferable to any language. Workshops will allow program development for problem solving in a Chemistry context.
Lectures will introduce multivariate statistics and data reduction methods for exploratory data analysis and data visualization. The ideas of correlation and regression will be covered and supervised learning algorithms for discrimination and classification introduced as well as clustering algorithms for unsupervised analysis. The emphasis will be on practical applications and workshops will allow the techniques to be applied to chemical data using the software packages available in R.
Lectures introduce the use of computers for information storage and management in the molecular context, including: the types of data available, how data is represented and interrogated, and how this information is used to solve problems in Chemistry. Topics include molecular representation and property descriptors, including the SMILES and SMARTS languages for describing molecular patterns, the use of databases and methods for the comparison of molecular structures.
Chemistry Core Modules 1-9
A written paper (50%) plus workshop assessment and project work (50%)