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Demonstration
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
Kexin Huang · Tianfan Fu · Dawood Khan · Ali Abid · Ali Abdalla · Abubaker Abid · Lucas Glass · Marinka Zitnik · Cao Xiao · Jimeng Sun

Tue Dec 08 07:20 AM -- 07:40 AM & Wed Dec 09 07:20 AM -- 07:40 AM (PST) @
in Demonstrations 1 »
Event URL: http://deeppurpose.sunlab.org/ »

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.

Author Information

Kexin Huang (Harvard University)
Tianfan Fu (Georgia Institute of Technology)
Dawood Khan (Gradio)
Ali Abid (Gradio)
Ali Abdalla (Gradio)
Abubaker Abid (Gradio)
Lucas Glass (IQVIA)
Marinka Zitnik (Harvard University)
Cao Xiao (Iqvia)
Jimeng Sun (University of Illinois Urbana-Champaign)

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