The presence of endocrine disrupting contaminants (EDCs) in the environment has been known for decades and is due to anthropogenic activities [1]. They can induce adverse effects even at very low concentrations [2,3]. However, the identification, detection, and quantification of EDCs remain several challenges. First, mechanisms of endocrine disruption are very complex through multifaceted pathways [4]. Among these mechanisms, the ability to bind to and/or activate nuclear hormone receptors (NRs) is essential to identify endocrine disruptor hazards [5]. Second, the EDCs belong to a highly heterogeneous group of chemicals, which may have very different environmental behaviors [6,7]. Therefore, the unique characteristics of each compound make it more challenging to find distinct structure-activity relationships of EDCs [6]. The study aims at proposing an alternative, easily exploitable clustering approach to link the chemical structure of organic compounds and their ED properties, and eventually at highlighting the relationship with their environmental fate. The approach will be based on TyPol (Typology of Pollutants) which is an operational tool that allows the clustering of organic compounds according to similarities in molecular properties, fate, and effects [8]. For characterizing the ED effects in particular, binding affinities (BA) and bioactivity of compounds have been selected as new parameters to implement in the TyPol database. Furthermore, a set of molecular descriptors, environmental, and ecotoxicological parameters [8] will be employed in addition to BA and bioactivity. At first, TyPol will be used to classify organic contaminants that are known or supposed to exhibit estrogenic (249 compounds) and androgenic (104 compounds) properties. These compounds were retrieved from NORMAN (List of chemicals – S109/PARCEDC), EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), and CompTox Chemicals Dashboard. Finally, the TyPol tool is expected to discriminate potential EDCs of high concern from those of lower concern based on their aforementioned parameters in order to prioritize compounds and improve environmental risk assessment.