Chapter List

Preface
1. An Introduction to Artificial Intelligence
2. Artificial Neural Networks
3. Self-Organizing Maps
4. Growing Cell Structures
5. Cellular Automata
6. Evolutionary Algorithms
Genetic Algorithms
Genetic Programming
Particle Swarm Optimization
7. Expert Systems
8. Fuzzy Logic
9. Classifier Systems
10. Evolvable Developmental Systems

In each area, the principles of the method are discussed, together with an outline of how the method can be used to solve scientific problems. The text includes a CD.

Introduction to Artificial Intelligence

The number of compounds that could conceivably act as drugs is not infinitely large (many drugs act by locking into the active site of a protein, so need to be quite small, otherwise they would not fit into the active site). Nevertheless, the number of possibilities is substantial, roughly 10⁴⁰.

To assess such a large number of possibilities computationally, smart methods are needed to sift out potentially valuable drugs from all the rest. Artificial Intelligence offers tools that can help in this task.

Artificial Neural Networks

Artificial Neural Networks (ANNs) are probably the most widely used AI tool in the experimental sciences, since they can be trained to accomplish a considerable range of tasks. An ANN consists of a number of simple computational units which, on their own, can accomplish rather little, but may have great power when joined in a network.

Self-organizing Maps

The role of a self-organizing map is to crush multi-dimensional data down into a smaller number of dimensions (usually two), so that the clustering of datapoints in n-dimensional space is made apparent.

Here are two images drawn from the development of a Self-organizing map:

The figure on the right shows the structure of a self-organizing map mid-way through training. The colours are determined by interpreting the first three weights at each node as RGB values. On the left, the same set of weights is plotted, but this time the colours are determined by how different the weights at one node are from the weights in the small group of nodes that immediately surround it.

Self-organizing maps are widely used in science to group and classify data, particularly when the data are extremely complex, such as the mass spectra of multi-component mixtures like oils or biological samples.

Cellular Automata

Cellular Automata (CA) models are straightforward to use and intuitively simple. They break a region of simulation into a number of discrete regions, replacing the differential equations which would often be used to model dynamic systems by a large number of small, simple units. The most widely-known example of a CA model is probably John Conway's Game of Life.

Cellular Automata show potential in science in the modelling of systems that change in space, time, or both, such a bacterial colonies (below).

EJS - Easy Java Simulations

EJS ("Easy Java Simulations") has been used to prepare a number of the illustrations in the book. This tool, written by Prof Francisco Esquembre at the University of Murcia in Spain, provides a simple but powerful way to introduce professional graphics into Java. A full, free, version of EJS is provided on the CD that accompanies the text.

A screen shot from a typical EJS application - a simulation of particles in a three-dimensional box showing velocity traces and calculated forcefield - appears below.

Contact information

Hugh.Cartwright@chem.ox.ac.uk
telephone: +44-(0)1865-275483
fax: +44-(0)1865-275410