title: Designing lead optimisation of MMP-12 inhibitors.
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.
authors: European Centre for Living Technology, 30124 Venice, Italy ; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, 30123 Venice, Italy.
authors: Matteo
authors: M
authors: Borrotti
authors: European Centre for Living Technology, 30124 Venice, Italy ; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, 30123 Venice, Italy.
authors: Davide
authors: D
authors: De March
authors: European Centre for Living Technology, 30124 Venice, Italy ; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, 30123 Venice, Italy.
authors: Debora
authors: D
authors: Slanzi
authors: European Centre for Living Technology, 30124 Venice, Italy ; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, 30123 Venice, Italy.
authors: Irene
authors: I
authors: Poli
mesh_terms: D000465:Algorithms; D002626:Chemistry, Pharmaceutical / Q000379:methods*; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000737:chemistry*; D006801:Humans; D053508:Matrix Metalloproteinase 12 / Q000737:chemistry*; D061965:Matrix Metalloproteinase Inhibitors / Q000138:chemical synthesis / Q000737:chemistry*; D012984:Software
chemical_list: D004791:Enzyme Inhibitors; D061965:Matrix Metalloproteinase Inhibitors; C507122:MMP12 protein, human; D053508:Matrix Metalloproteinase 12