Abstract:
Lead Molecule Optimisation and the process of Drug Design are very difficult problems, demanding an huge investment in terms of resources and experimentation. The standard approach requires iterations of long synthesis and testing cycles as well as formulations, which are hard and complex tasks. In this thesis are presented, in the first chapters, an analysis of Nature inspired computation and the problem of Lead Molecule Optimisation; lately it is proposed a method to address such problem. It is the Genetic Algorithm Optimisation (GAO) method, which will be applied to a set of experimental data of fitness values and molecule fragments. The dataset is provided by the European Center for Living Technology (ECLT). The GAO is used to find an optimal solution for the development of a particular drug (MMP-12 inhibitors). A total of 120 molecules are tested over 6 generations; the results show that the algorithm works as intended by reaching the optimum in an acceptable amount of time, required for the computation. The good performance of the GAO method is analysed and shown in the thesis with its capacity to reach the desired optimum value using the data set provided.
