  The efficacy of a drug depends on its binding affinity to the therapeutic
target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable
progress in predicting drug efficacy. We develop MolDesigner, a
human-in-the-loop web user-interface (UI), to assist drug developers leverage
DL predictions to design more effective drugs. A developer can draw a drug
molecule in the interface. In the backend, more than 17 state-of-the-art DL
models generate predictions on important indices that are crucial for a drug's
efficacy. Based on these predictions, drug developers can edit the drug
molecule and reiterate until satisfaction. MolDesigner can make predictions in
real-time with a latency of less than a second.
