Biological, chemical, and physical properties of molecules are encoded in their molecular structure. The challenge lies in discovering the relationships between the structure of the molecular graphs and the measured activity. In this presentation, we introduce Chemaxon’s new product, the Trainer Engine. It is designed to streamline the workflow starting from input data containing measured activities until validated models are implemented for a wide audience. In addition to summarizing our results obtained with various machine learning model training scenarios, our goal is to highlight the model inference aspects. Accordingly, we present an integration use case with Chemaxon’s Design Hub. Connecting these applications widens the range of information resources available for decision-making on compound series to enhance drug discovery pipelines.