Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results

Human inspection of potential drug compounds is crucial in the virtual drug screening pipeline. However, there is a pressing need to accelerate this process, as the number of molecules humans can realistically examine is extremely limited relative to the scale of virtual screens. Furthermore, computational medicinal chemists can evaluate different poses inconsistently, and there is no standard way of recording annotations. We propose Autoparty, a containerized tool to address these challenges. Autoparty leverages on-premises active learning for drug discovery to facilitate human-in-the-loop training of models that extrapolate human intuition. We leverage multiple uncertainty quantification metrics to query the user with informative examples for model training, limiting the number of human expert training labels. The collected annotations populate a persistent and exportable local database for broad downstream uses. Incorporating Autoparty resulted in a 40% increase in hit rate over shape similarity alone among 193 experimentally tested compounds in a real-world case study.