Chemogenomics Lab
The Chemogenomics Laboratory is dedicated to guiding and supporting drug discovery, drug development, and personalized medicine through the development and application of in silico tools and strategies.
Therefore research carried out in the Laboratory articulates around two major axes:
1. Computer-aided drug discovery and development. Computational tools and strategies are utilized towards the identification of active small molecules and towards the study of their modes of interaction across families or subfamilies of disease-relevant targets. These active molecules are either used as molecular probes for validating targets from molecular, functional, and structural genomics, or serve as hits for lead generation in the drug discovery process.
2. Scientific data management and mining. Data management and data mining strategies are explored and exploited towards the identification of disease-relevant genomic information and its integration with chemical information.
The main goal of the Chemogenomics Laboratory is to facilitate the discovery of active small molecules capable of targeting disease-specific contexts of vulnerabilities, which will have a significant impact on further development of personalized medicine.
Staff expertise:
Expertise of the staff covers key aspects of computer-aided drug discovery, including cheminformatics, ligand- and structure-based design, protein modeling, molecular similarity, and data mining.
Strategies:
Chemoinformatic and data mining techniques are applied to analyze high-throughput screening datasets, to identify active chemotypes, to design focused compound libraries for further experimental screening, and to determine relationships between molecular structure and activity, biological response, or phenotypic data.
Molecular design methods are utilized to build homology models of relevant targets, to explore ligand-protein, protein-protein, or protein-DNA interactions in structural data, to assess possible ligand binding modes using molecular docking approaches, to elucidate pharmacophore models, to carry out virtual ligand screening for further compound selection/prioritization, and to generate functional/structural hypotheses for further experimental validations.
Data mining strategies also serve a number of other applications such as identifying interesting differential gene-expression profiles, unveiling genomic aberrations associated with drug response in data from cellular assay or directly from patients, or merging clinical and functional data to discover disease biomarkers in patient populations.
Scientific collaborations:
In silico research and data mining activities at the Chemogenomics Laboratory are carried out in close conjunction with other laboratories within the Pharmaceutical Genomics Division and division-wide within TGen, and with extra-mural scientific communities as well. To accomplish its mission, the Laboratory values and encourages current and future collaborative initiatives.
Staff:
Spyro Mousses, Ph.D., Unit Head
Joachim Petit, Ph.D., Staff Scientist
Nathalie Meurice, Ph.D., Associate Investigator / Computational scientist
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