MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning

• URL: http://arxiv.org/abs/2010.03951v1
• Date: Mon, 5 Oct 2020 21:25:25 GMT
• Title: MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
• Authors: Kexin Huang, Tianfan Fu, Dawood Khan, Ali Abid, Ali Abdalla, Abubakar Abid, Lucas M. Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun
• Abstract summary: MolDesigner is a human-in-the-loop web user-interface (UI) for drug developers. 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.
• Score: 61.74958429818077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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