2010/02/17 by Gregory R. Bowman, Daniel L. Ensign, Vijay S. Pande · 8 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Mathematics · #Protein Structure and Dynamics #Machine Learning in Materials Science #Gene Regulatory Network Analysis #Computer science #Markov chain #Metric (unit) #Sampling (signal processing) #Markov process #Markov model #Convergence (economics) #Theoretical computer science #Algorithm #Statistical physics #Machine learning #Mathematics #Physics
paper · doi:10.1021/ct900620b
openalex publication_date 2010/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short timescales, leaving modeling of most biologically relevant systems and timescales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing previously intractable calculations to be performed on a routine basis on existing hardware. We also introduce a distance metric (based on the relative entropy) for comparing MSMs. We primarily employ this metric to judge the convergence of various sampling schemes but it could also be employed to assess the effects of perturbations to a system (e.g. determining how changing the temperature or making a mutation changes a system's dynamics).