Structural Bioinformatics, Chemoinformatics, Data Mining & Machine Learning

Team

  • Bruno Villoutreix, DR Inserm
  • Dr. Natesh Singh (Postdoctoral fellow)
  • Dr. Ludovic Chaput (Postdoctoral fellow)

Links

  • Visit our directory of computational Approaches and "Free" Resources to Assist Drug Discovery, Chemical Biology & Precision Medicine

Computational approaches play an important role in drug discovery and chemical biology.

Different strategies and methods are being developed and/or used in the laboratory.

  • Virtual screening, ligand-based and/or structure-based methods, is used to predict potential binders and for hit to lead optimization. We work on the optimization of these methods. These strategies are combined in a rational manner with experimental HTS endeavors. We use different compound collections as input, the lab’s library, various commercial collections or collections containing approved drugs, virtual molecules, etc. This selection is depending on the project and the stage of the project.
  • Computation of ADME-Tox properties exploiting statistical techniques among others reduces the number of expensive assays that must be performed. It is possible to predict some of these properties early in the discovery process so as to minimize late stage failures. These approaches now benefit from the so-called -omics data deluge and from data obtained in the laboratory via the ADME platform. We use existing approaches (eg., FAF-Drugs) and we develop novel approaches in the laboratory, from ADME-Tox predictions to compound profiling. These computations are associated with in silico multi-parameter optimizations (MPO) where we attempt to optimize in parallel several properties of the hits identified in silico and/or via HTS.
  • Various structural bioinformatics algorithms are being used to investigate protein targets, from point mutations observed in patients to druggable pockets.
  • Data mining and machine learning strategies are used whenever appropriate to explore data and to transform data into knowledge and then to actionable knowledge. This for instance involves the exploration of the chemical space with a special emphasis on protein-protein interaction inhibitors.
  • Integration of these approaches is essential. We combine the best strategies and apply them on the various experimental studies carried out in the lab.
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