学术讲座
报告题目：Accelerated Molecular Simulations: Principles and Applications （加速分子动力学：原理和应用）
报告时间：2023-08-02 16:00
报告人： Yinglong Miao（缪应龙）副教授
University of Kansas
报告地点：曾呈奎楼B311
转播地点：翔安校区能源材料大楼3号楼会议室5，漳州校区生化主楼307教室
报告摘要：
Remarkable advances of supercomputing and Artificial Intelligence are transforming computational chemistry and biology in studies of molecules to cells. However, large gaps remain between the time scales of supercomputer simulations (typically microseconds) and those of biological processes (milliseconds or even longer). To bridge these gaps, our research is focused on the development of novel computational methods and Deep Learning (DL) techniques, including Gaussian accelerated molecular dynamics (GaMD) and Deep Boosted Molecular Dynamics (DBMD). Our recently developed selective GaMD algorithms have unprecedentedly enabled microsecond atomic simulations to capture repetitive dissociation and binding of small-molecule ligands, highly flexible peptides and proteins, thereby allowing for highly efficient and accurate calculations of their binding free energies and kinetics. Moreover, the GaMD, DL and free energy prOfiling Workflow (GLOW) provides a systematic approach to predicting important molecular determinants and quantifying free energy profiles of biomolecules. In DBMD, probabilistic Bayesian neural network models are implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, which enables more accurate energetic reweighting and further enhanced simulations. Finally, we apply our these new methods in advanced biomolecular modeling and computer-aided drug discovery. In collaboration with leading experimental groups, we combine complementary simulations and experiments to decipher functional mechanisms and design novel drug molecules of important biomolecules. Systems of our interest include membrane proteins such as G-protein-coupled receptors and membrane-embedded proteases, RNA-Binding Proteins and RNA.
报告人简介：
缪应龙，美国堪萨斯大学University of Kansas分子生物科学系和计算生物学中心副教授，2023年 8月将加入美国北卡大学University of North Carolina-Chapel Hill医学院药理学系和计算药物专业。实验室目前专注于计算生物和化学的方法开发，并将这些方法应用于生物分子的高级模拟和药物发现；“高斯加速分子动力学”创始人，2017年获得美国心脏协会“科学家发展奖”，2021 年获得美国化学会计算化学“OpenEye杰出青年教师奖”；至今在Nature, Nature Communications, PNAS, JACS等国际重要刊物上发表了100 多篇学术论文，研究获得了美国心脏协会、NIH和NSF的资助。
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