The University of Southampton

CHEM6128 Data-Driven Science

Module Overview

________________________________________ The rapid rise of Chemical Informatics or Chemoinformatics over the last decade means that it is now an essential tool in the chemical and pharmaceutical industries, informing decisions throughout the whole range of chemical applications. The course covers the basic ideas of chemical informatics applied across the whole range of chemistry, for example in describing and manipulating molecular structure & properties, analysis and model building, and covering applications ranging from pharmaceuticals to pesticides. The required statistical background is developed in the course within a chemical context and the need to represent information in a way that computers as well as humans can work with the data will be explored in the context of automation of experimentation and analysis, making full use of the current e-science approaches to scientific experimentation and analysis. The course will provide hands on experience at building models to describe chemical properties and give experience in presenting this information.

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Have a knowledge of chemical informatics techniques
  • Be able to use of chemical informatics techniques in industrial and academic research application areas
  • be able to apply chemical informatics techniques to chemical design and experimental investigations


The syllabus, which is described in outline below, is aligned with the following QAA benchmark statements for chemistry at FHEQ Level 7 (Masters). • to extend students' comprehension of key chemical concepts and so provide them with an in-depth understanding of specialised areas of chemistry; • to develop in students the ability to adapt and apply methodology to the solution of unfamiliar types of problems; • to instill a critical awareness of advances at the forefront of the chemical science discipline; • to prepare students effectively for professional employment or doctoral studies in the chemical sciences; • the ability to adapt and apply methodology to the solution of unfamiliar problems; • knowledge base extends to a systematic understanding and critical awareness of topics which are informed by the forefront of the discipline; • problems of an unfamiliar nature are tackled with appropriate methodology and taking into account the possible absence of complete data. The lecture content will include 1. Data Analysis • Modern techniques of data analysis • The use of model fitting and prediction 2. Cheminformatics • The representation of structure • The representation of chemical information • The understanding of molecular and chemical descriptors 3. QSAR and Chemometrics • The applications of quantitative structure property relations • An introduction to Statistical Design of Experiments • The design and use of virtual screening • The design and analysis of combinatorial libraries

Learning and Teaching

Teaching and learning methods

Teaching Methods • Lectures • Workshops • Computational practical work • Using chemical informatics websites and software • Directed reading • Bb online support Learning Methods Independent study, independent website design and construction, student motivated peer-group study, student driven tutor support.

Preparation for scheduled sessions20
Follow-up work27
Practical classes and workshops6
Total study time75

Resources & Reading list

J. Gasteiger and T. Engel (2003). Cheminformatics. 

A.R. Leach and V.J. Gillet (2003). An Introduction to Cheminformatics. 



MethodPercentage contribution
Examination  (1 hours) 70%
Project 30%


MethodPercentage contribution
Examination  (1 hours) 70%
Project 30%
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