SELECT * FROM pwn_ihpcf_person_info WHERE ihpcfusername = 'JunXu' Jun XuProfessor, School of Pharmaceutical, Sun Yat-sen University

Jun Xu

Professor, School of Pharmaceutical, Sun Yat-sen University

Jun Xu has completed his PhD from University of Science & Technology of China (USTC) and postdoctoral studies from Australian National University (ANU) and McGill University. He is the founding director of Research Center for Drug Discovery(RCDD), which consisits of Bio/Chemo-informatics lab, Drug Design lab, Phyto/Medicinal Chemistry lab, Structure biology lab, and Drug Screening lab. He has published more than 90 papers in reputed journals and has been serving as an editorial board member of a number of reputed international journals. He is also the adjunct Professor of University of Pittsberg (Pittsberg, USA) and RMIT Univeristy (Melbourne, Australia) and Wuyi University. Dr. Xu and his colleagues have developed HPC algorithms for chemical structure recognitions, classifications, drug-likeness, 3D molecular superimposing, virtual drug screening, new technologies for natural product extractions and syntheses. His algorithms have been used in main-stream molecular design software systems in the world. The research projects on HPC supported algorithms for pharmaceutical innovations are funded by The Ministry of Science & Technology of China and Chinese National Scientific Foundation.


Talk Title:

HPC Accelerated Algorithms for Pharmaceutical Innovations: Progresses and Perspectives


Talk Abstract:

Pharmaceutical innovations involve in design, manufacture, and biologically validate the functions of small- and macro-molecules against specific diseases. Making or assaying these molecules are costly and time-consuming. In past decades, many algorithms have been developed to expedite the innovation processes and reduce the costs. These biomolecules are mathematically expressed in topological, steric, and static levels, which have different types of computational complexities. This talk will outline the algorithm problems in this field, summarize the progresses on HPC accelerated algorithms for pharmaceutical innovations in the author’s team. The algorithms, such as, subgraph such/match algorithms, high frequent substructure deriving algorithm, virtual high throughput screening algorithms, virtual compound library generation algorithms, compound library generation algorithms, and artificial intellectual algorithms for druggability predictions, and their parallelization are specifically discussed in this lecture. Case studies on drug design or virtual screening using machine learning approaches, such as, naїve Bayesian classification, support vector machine and recursive petitioning, are presented. Biomedical big data processing technology including parallelized and GPU-accelerated molecular dynamics simulation technology, enhanced molecular docking technology, new parallelized algorithms for shape-based virtual screening, and free energy landscape calculations will be discussed. It is expected that the future developments in this field will focus on the new modeling methodologies capable of handling big data created from high throughput scientific experiments (screening and manufacturing), high performance computations (parallel computing based molecular simulation), medical information (such as medical record and health information) automation, and scientific publication/patent literature digitalization.


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