首页|Institute of Computer Science Reports Findings in Machine Learning (Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics)
Institute of Computer Science Reports Findings in Machine Learning (Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Brno, Czech Republic, by NewsRx correspondents, research stated, “The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods.” Our news editors obtained a quote from the research from the Institute of Computer Science, “Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage trajectory (tryptophan cage) from the literature. The trajectory was used to generate a map of conformations, distinguish fast conformational changes from slow ones, and design CVs. Then, metadynamic simulations were performed. To accelerate the formation of the a-helix, we added the a-RMSD collective variable. This simulation led to one folding event in a 350 ns metadynamics simulation. To accelerate degrees of freedom not addressed by CVs, we performed parallel tempering metadynamics.”
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