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.
@article{arxiv.2010.03951,
title = {MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning},
author = {Kexin Huang and Tianfan Fu and Dawood Khan and Ali Abid and Ali Abdalla and Abubakar Abid and Lucas M. Glass and Marinka Zitnik and Cao Xiao and Jimeng Sun},
journal= {arXiv preprint arXiv:2010.03951},
year = {2020}
}