Molecular Dynamics
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You may be a new researcher who has recently developed an interest in computational chemistry and would like to learn more about the MD simulation technique. You may be an experimentalist eager to perform MD simulations to support your experiments. Regardless of your background, this primer is a good starting point. Like many interdisciplinary technologies, there is a learning curve for newcomers that includes some advanced math and physics pertinent to MD simulations. This primer aims to explain key concepts while minimizing the use of equations to balance the introduction of theory with practical applications. It consists of three chapters: Chapter 1 presents the fundamentals of MD simulations; Chapter 2 introduces enhanced sampling techniques; Chapter 3 discusses the applications of MD in discovering new materials and medicines. We also include sidebar boxes, figures, and extended reading materials to help our readers.
As you read this primer, consider the potential impact of MD simulations in your research field. When you dive deeply into your investigations, consider how MD can offer new insights into the phenomena you are studying. Today, the power of MD simulations has far surpassed what was once thought possible, particularly with the emergence of AI/ML. As long as you have good ideas, the potential applications for MD in discovery and innovation are limitless.
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Jianing Li is a Professor in the Borch Department of Medicinal Chemistry and Molecular Pharmacology (BMCMP) at Purdue University. She earned her B.S. in Chemical Physics from the University of Science and Technology of China in 2006 and her Ph.D. in Chemical Physics from Columbia University in 2011. After her postdoctoral training at the University of Chicago, she joined the Department of Chemistry at the University of Vermont in 2014 and moved to Purdue BMCMP in 2022. Part of her current research focuses on multiscale modeling and simulation, which allows accurate design of nanoparticles as potential therapeutics and for drug delivery. She has received the 2019 OpenEye Junior Faculty Award in Computational Chemistry, the 2020 NSF CAREER Award, and the 2023 AnalytiXIN Fellowship.
Xianshi Liu is a scientist with expertise in the intersection of computational physics, molecular biology, and artificial intelligence. He received his B.E. in Physics from Fudan University in 2016. He later completed his Ph.D. in Computational Physical Biology at Fudan University in 2023, where his research focused on the molecular mechanisms of peptide aggregation and liquid–liquid phase separation. In 2023 and 2024, he was a postdoctoral reasearcher in Dr. Jianing Li’s laboratory at Purdue University. He currently focuses on developing AI-driven algorithms for peptide generation and exploring the interaction mechanisms within complex biomolecular systems through molecular dynamics simulations with enhanced sampling techniques.
