We have built an automated, workflow-based system that predicts mechanism of action for new indications of safe, off-patent drugs. The platform technol. can also design new mols. for a known target or an active drug program. We do this through a combination of enumerating derivatives from a patent, generating a combinatorial library of analogs around a Markush scaffold, chem. fingerprint searches, 3D similarity (shape, pharmacophores, electrostatics), ADMET descriptor matching, gene expression profiling, and protein docking. The platform is built in the KNIME workflow environment, and uses both open source as well as proprietary software. The prediction algorithm is custom designed using machine learning models that have been trained on large data sets. We connect and make use of multiple web-accessible databases including those for binding activity, chem. and protein structures, biol. pathways, and gene expression. To feed compounds into the workflow, we have also built a comprehensive compound registration system that analyses, isomerizes, de-duplicates, and uploads compounds to an Instant JChem-enabled MySQL database server. Our base library consists of 10,000 com. available drug compounds, as well as several hundred hand-picked compounds with known activities. Our workflow-based platform technol. has proven especially useful when partnering with small and mid-size pharmaceutical companies seeking to address an unmet medical need by redesigning an existing product, and where regulatory approval is likely to be achieved rapidly. We provide an example of this platform being used successfully to repurpose an antipsychotic mol. into a drug candidate currently in Phase III clin. trials. We are currently in the process of designing better mol. analogs for this project.