SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction.

Options to improve the extrapolation power of the neural network designed using the SchNetPack package with respect to top docking scores prediction are presented. It is shown that hyperparameter tuning of the atomistic model representation (in the schnetpack.representation) improves the prediction of the top scoring compounds, which have characteristically a low incidence in randomized data sets for training of machine learning models.

Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus.

Molecular docking of 234 unique compounds identified in the softwood bark (W set) is presented with a focus on their inhibition potential to the main protease of the SARS-CoV-2 virus 3CL (6WQF). The docking results are compared with the docking results of 866 COVID19-related compounds (S set). Furthermore, machine learning (ML) prediction of docking scores of the W set is presented using the S set trained TensorFlow, XGBoost, and SchNetPack ML approaches.

Machine learning prediction of 3CL SARS-CoV-2 docking scores.

Molecular docking results of two training sets containing 866 and 8,696 compounds were used to train three different machine learning (ML) approaches. Neural network approaches according to Keras and TensorFlow libraries and the gradient boosted decision trees approach of XGBoost were used with DScribe's Smooth Overlap of Atomic Positions molecular descriptors. In addition, neural networks using the SchNetPack library and descriptors were used.

Formation and fragmentation of the tungsten clusters in gas phase.

We present a theoretical study of accumulation of clusters consisting of up to 100 tungsten atoms based on information extracted from molecular dynamics trajectory simulations. The description is based on the rates corresponding to the single W atom attachment to W clusters and their dissociation processes. The results display a strong Arrhenius dependence of the dissociation rate constant on temperature.

Ab initio X(1)0(+) ground state potential curves of Pb···RG dimers (RG = He, Ne, Ar) including spin-orbit effects. Simulation of diffusion coefficients.

CCSD(T) ground state potential curves of Pb···RG systems (RG = He, Ne and Ar) are presented and the importance of the inclusion of spin-orbit effects is discussed. The closed-shell character of the Pb atom at the two-component relativistic level of relativistic theory leads to shallower potential energy curves compared to scalar relativistic open-shell calculations. The pressure-independent cross-diffusion coefficients pD12 have been simulated using the extrapolated two-component CCSD(T) ground state potential curves.

Specific O₂⁻ generation in corona discharge for ion mobility spectrometry.

This study deals with O(2)(-) generation in corona discharge (CD) in point to plane geometry for single flow ion mobility spectrometry (IMS) with gas outlet located behind the ionization source. We have designed CD of special geometry in order to achieve the high O(2)(-) yield. Using this ion source we have achieved in zero air conditions that up to 74% all negative ions were O(2)(-) or O(2)(-)(H(2)O).