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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 258627, 8 pages
http://dx.doi.org/10.1155/2014/258627
Research Article

Designing Lead Optimisation of MMP-12 Inhibitors

1European Centre for Living Technology, 30124 Venice, Italy
2Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30123 Venice, Italy

Received 9 October 2013; Revised 16 December 2013; Accepted 16 December 2013; Published 12 January 2014

Academic Editor: Rudolf Füchslin

Copyright © 2014 Matteo Borrotti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The design of new molecules with desired properties is in general a very difficult problem, involving heavy experimentation with high investment of resources and possible negative impact on the environment. The standard approach consists of iteration among formulation, synthesis, and testing cycles, which is a very long and laborious process. In this paper we address the so-called lead optimisation process by developing a new strategy to design experiments and modelling data, namely, the evolutionary model-based design for optimisation (EDO). This approach is developed on a very small set of experimental points, which change in relation to the response of the experimentation according to the principle of evolution and insights gained through statistical models. This new procedure is validated on a data set provided as test environment by Pickett et al. (2011), and the results are analysed and compared to the genetic algorithm optimisation (GAO) as a benchmark. The very good performance of the EDO approach is shown in its capacity to uncover the optimum value using a very limited set of experimental points, avoiding unnecessary experimentation.