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The efficacy of a drug depends on its binding affinity to the therapeutictarget and pharmacokinetics. Deep learning (DL) has demonstrated remarkableprogress in predicting drug efficacy. We develop MolDesigner, ahuman-in-the-loop web user-interface (UI), to assist drug developers leverageDL predictions to design more effective drugs. A developer can draw a drugmolecule in the interface. In the backend, more than 17 state-of-the-art DLmodels generate predictions on important indices that are crucial for a drug'sefficacy. Based on these predictions, drug developers can edit the drugmolecule and reiterate until satisfaction. MolDesigner can make predictions inreal-time with a latency of less than a second.

MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning