Antioxidant effect, DNA-binding, and transport of the flavonoid acacetin influenced by the presence of redox-active Cu(II) ion: Spectroscopic and in silico study.

Acacetin (AC) is a natural polyphenol from the group of flavonoids. It is well established that the behavior of flavonoids depends on the presence of redox-active substances; therefore, we aim to investigate their biological activity following the interaction with Cu(II) ion. Our study demonstrates that AC can effectively bind Cu(II) ions, as confirmed by UV-Vis and EPR spectroscopy as well as DFT calculations.

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.

Compromise in Docking Power of Liganded Crystal Structures of M SARS-CoV-2 Surpasses 90% Success Rate.

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (M). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.

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.

3CL and PL affinity, a docking study to fight COVID19 based on 900 compounds from PubChem and literature. Are there new drugs to be found?

The spread of a novel coronavirus SARS-CoV-2 and a resulting COVID19 disease in late 2019 has transformed into a worldwide pandemic and has effectively brought the world to a halt. Proteases 3CL and PL, responsible for proteolysis of new virions, represent vital inhibition targets for the COVID19 treatment. Herein, we report an docking study of more than 860 COVID19-related compounds from the PubChem database.