Simulation numérique et approche orientée connaissance pour la découverte de nouvelles molécules thérapeutiques

Leo Ghemtio 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Therapeutic innovation has traditionally benefited from the combination of experimental screening and molecular modelling. In practice, however, the latter is often limited by the shortage of structural and biological information. Today, the situation has completely changed with the high-throughput sequencing of the human genome, and the advances realized in the three-dimensional determination of the structures of proteins. This gives access to an enormous amount of data which can be used to search for new treatments for a large number of diseases. In this respect, computational approaches have been used for high-throughput virtual screening (HTVS) and offer an alternative or a complement to the experimental methods, which allow more time for the discovery of new treatments. HTVS methods can be used on large molecular databases, to greatly reduce the experimental time and cost by supplying potentially interesting molecules for every desired aimed biological target. However, most of these approaches suffer the same limitations. One of these is the cost and the computing time required for estimating the binding of all the molecules from a large data bank to a target, which can be considerable in the context of the high-throughput. Also, the accuracy of the results obtained is another very evident challenge in the domain. The need to manage a large amount of heterogeneous data is also particularly crucial. To try to surmount the current limitations of HTVS and to optimize the first stages of the drug discovery process, I set up an innovative methodology presenting two advantages. Firstly, it allows to manage an important mass of heterogeneous data and to extract knowledge from it. Secondly, it allows distributing the necessary calculations on a grid computing platform that contains several thousand of processors. The whole methodology is integrated into a multiple-step virtual screening funnel. The purpose is the consideration, in the form of constraints, of the knowledge available about the problem posed in order to optimize the accuracy of the results and the costs in terms of time and money at various stages of high-throughput virtual screening. The methodological approaches that I developed were successfully applied to study the problem of HIV resistance to antiviral therapy. This project was supported by the Bill and Melinda Gates Foundation within the framework of a project of collaboration with the CIRCB in Cameroon.
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Leo Ghemtio. Simulation numérique et approche orientée connaissance pour la découverte de nouvelles molécules thérapeutiques. Autre. Université Henri Poincaré - Nancy 1, 2010. Français. ⟨NNT : 2010NAN10103⟩. ⟨tel-01748659v2⟩

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