Le criblage virtuel est de plus en plus utilisé dans les programmes de recherche de nouveaux principes actifs. L’augmentation considérable du nombre de structures résolues a favorisé le recours aux méthodes basées sur la structure de la cible comme le docking. Néanmoins, le choix de la/des structure(s) à utiliser demeure une question d’actualité. Pour tenter d'apporter une réponse, les résultats des études de docking menées sur la banque d’évaluation de référence (DUD) ont été analysés en prenant en compte les propriétés des sites de liaisons des structures de référence. D’intéressants résultats ont été obtenus mettant en évidence l'influence du volume et de l’ouverture des sites actifs sur les performances des méthodes. Ces critères de sélection simples et peu coûteux peuvent servir pour l’optimisation de protocoles de docking.Alors qu’aucune petite molécule inhibitrice du TNFα n’est actuellement commercialisée, l’application d’un protocole hiérarchique de criblage virtuel/in vitro, a permis d’identifier des touches actives. L’une d’elle, de squelette benzènesulfonamide a fait l’objet de pharmacomodulation en vue d’obtenir des analogues optimisés. Vingt molécules inédites ont été synthétisées et testées in vitro et certaines ont montré une activité intéressante. L’ensemble des données obtenues apportent des éléments importants de relation structure-activité. Ces résultats peuvent être exploités pour la conception de molécules innovantes ciblant le TNFα ce qui serait une avancée prometteuse pour le traitement des pathologies liées à une surproduction de cette cytokine comme la polyarthrite rhumatoïde et la maladie de Crohn. / Virtual screening is widely used in drug discovery programs. The increasing number of resolved structures favored the use of Structure Based Virtual Ligand Screening methods like docking. Nevertheless, the choice of the structure(s) used as reference remains a topical issue when several are available. In this work, DUD database docking results were analyzed taking into account the properties of the query structure(s) binding sites. Interesting results were obtained highlighting the influence of active site volume and opening on methods performances. These simple and inexpensive “binding site properties-based” guidelines could be helpful to optimize future docking protocols.Despite important effort, no active small molecule targeting TNFα has been released so far. The use of a virtual/ in vitro hierarchical approach screening allowed identifying some active hits. Starting from one of them with a benzenesulfonamide structure, pharmacomodulation was achieved in order to obtain optimized analogs. Twenty new chemical derivatives with an original structure were synthesized and tested in vitro. Some of them exhibited an interesting activity. Moreover, data obtained provide important elements of structure-activity relationship. These results could constitute the basis for innovative small molecule TNFα-targeted therapeutics which would be a promising step for the treatment of diseases related to overproduction of this cytokine such as rheumatoid arthritis and Crohn's disease.

Government initiatives for more transparency and participation have lead to an increasing amount of structured data on the web in recent years. Many of these datasets have great potential. For example, a situational analysis and meaningful visualization of the data can assist in pointing out social or economic issues and raising people’s awareness. Unfortunately, the ad-hoc analysis of this so-called Open Data can prove very complex and time-consuming, partly due to a lack of efficient system support.On the one hand, search functionality is required to identify relevant datasets. Common document retrieval techniques used in web search, however, are not optimized for Open Data and do not address the semantic ambiguity inherent in it. On the other hand, semantic integration is necessary to perform analysis tasks across multiple datasets. To do so in an ad-hoc fashion, however, requires more flexibility and easier integration than most data integration systems provide. It is apparent that an optimal management system for Open Data must combine aspects from both classic approaches. In this article, we propose OPEN, a novel concept for the management and situational analysis of Open Data within a single system. In our approach, we extend a classic database management system, adding support for the identification and dynamic integration of public datasets. As most web users lack the experience and training required to formulate structured queries in a DBMS, we add support for non-expert users to our system, for example though keyword queries. Furthermore, we address the challenge of indexing Open Data.