There are two main objectives of the paper. First, we demonstrate that natural language processing (NLP) tools can be used to get a structured database on solvents from a continuously growing corpus of scientific articles. Next, we show that some of these solvents could potentially be endocrine disrupting chemicals (EDCs). This screening is done with the help of a deep-learning model that gives a multi-output binary classification. The model broadly classifies the solvents into two classes of EDCs -Agonist and Binding. For either of the classes, the solvents can further be categorized as "Active" or "Inactive" endocrine disruptors. If a solvent is classified as "Active" in any one of the classes, then it is a potential EDC. A solvent classified as "Inactive" in both the classes is highly unlikely to be an EDC. In addition, we also perform an uncertainty quantification of the classification to capture the model error.