Thanks to the constant growth of bioactivity databases and widespread utilization of graphical processing units (GPUs) the application of powerful data-driven approaches based on deep neural networks (DNNs) has grown substantially. DNNs found many use cases in molecular virtual screening and de novo compound generation (Figure ) . This rapidly evolving class of algorithms has been influencing modern drug discovery by building more accurate QSAR models , creating better molecular representations , predicting 3D protein structure with impressive accuracy or achieving other promising results in many medicinal and clinical applications . Depending on the architecture, the network is trained either as a bioactivity predictor (e.g. a QSAR model) or as a molecular generator.