Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as
one or more unwanted interactions may be associated with safety issues.
Contributions: We present an open-source, extendable web service for predicting target profiles with
confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking
scores from a large virtual library. The method uses conformal prediction to produce valid measures of
prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical
structures to the panel of targets with QuickVina on individual compound basis.
Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each
of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking
scores against an external validation set. The implementation as publicly available microservices on Kubernetes
ensures resilience, scalability, and extensibility