Jana Pazúriková, Jaroslav Oľha, Aleš Křenek et al.
Molecular dynamics simulates the~movements of atoms. Due to its high cost, many methods have been developed to "push the~simulation forward". One of them, metadynamics, can hasten the~molecular dynamics with the~help of variables describing the~simulated process. However, the~evaluation of these variables can include numerous mean square distance calculations that introduce substantial computational demands, thus jeopardize the~benefit of the~approach. Recently, we proposed an~approximative method that significantly reduces the~number of these distance calculations. Here we evaluate the~performance and the~scalability on two molecular systems. We assess the~maximal theoretical speed-up based on the reduction of distance computations and Ahmdal's law and compare it to the~practical speed-up achieved with our implementation.
Identification of experimentally acquired mass spectra of unknown compounds presents a~particular challenge because reliable spectral databases do not cover the potential chemical space with sufficient density. Therefore machine learning based \emph{de-novo} methods, which derive molecular structure directly from its mass spectrum gained attention recently. We present a~novel method in this family, addressing a~specific usecase of GC-EI-MS spectra, which is particularly hard due to lack of additional information from the first stage of MS/MS experiments, on which the previously published methods rely. We analyze strengths and drawbacks or our approach and discuss future directions.