Date：26 July (Wednesday)
Time：13:30 – 17:00 (GMT+8)
Digital Health Group
Development Center for Biotechnology
Since 2014, Dr. Ke has set up his chemical compound library and focused on the application of artificial intelligence in drug development and research. He has also generated an in-house technology service platform for antibody protein engineering. To date, three pharmaceutical companies have used this technology to optimize their antibody products. Dr. Ke’s major research activities within DCB include: 1. Computer-aided small molecule drug design. (e.g., docking; virtual high-throughput screening; structure-based, fragment-based, or ligand-based drug design; protein homology modeling; 3D-QSAR, molecular dynamics (MD); ADMET prediction; and QM/MM quantum mechanical calculation); 2. Computer-aided antibody engineering (e.g., humanization, removal of post-translational modifications (PTMs), removal of protein aggregation sites, removal of protease cleavage sites, T-cell epitope prediction, affinity maturation, etc.); 3. Artificial Intelligence and cheminformatics-aided drug discovery (e.g., target identification, drug activity prediction, and de novo drug design). To date, Dr. Ke has published 37 related articles in journals listed in the Science Citation Index (SCI) and has 4 drug patents.
The AI-aided Drug Design and Drug Development platform is a new generation of drug development and design technology that combines traditional computer-aided drug design (CADD) with artificial intelligence (AI) techniques. This technology platform can be applied to small molecule drug design (e.g. drug molecule docking, virtual high-throughput drug screening, structure-based drug design, drug modification, pharmacokinetic prediction, etc.), antibody drug design (e.g. humanization of antibodies, post-translational modification (PTM) removal, protein aggregation site removal, protease cleavage site removal, T-cell binding site prediction, affinity improvement, etc.), nucleic acid drug design (5', 3' UTR design, codon optimization, off-target analysis, ASO design, siRNA design, etc.), target search, repurposing application of existing drugs, prediction of new drug indications, and definition of clinical data and biomarkers analysis. The AI-assisted drug development technology platform established by DCB can assist both the industry and academia in their drug development processes and research.