Author: Huang, Kexin; Fu, Tianfan; Khan, Dawood; Abid, Ali; Abdalla, Ali; Abid, Abubakar; Glass, Lucas M.; Zitnik, Marinka; Xiao, Cao; Sun, Jimeng
Title: MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning Cord-id: nbqn1xjv Document date: 2020_10_5
ID: nbqn1xjv
Snippet: 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
Document: 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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