2020/10/05 by Kexin Huang, Tianfan Fu, Huang, Kexin +17
Computer Science · Immunology and Microbiology · Materials Science · #Biosimilars and Bioanalytical Methods #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2010.03951
openalex publication_date 2020/10/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.