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Dengue fever is a disease spread by the DENV virus through mosquitoes. This disease is dangerous because there is no specific drug, vaccine, or antiviral against the DENV virus, insisting on drug discovery for dengue fever. RNA-dependent RNA polymerase (RdRp) enzyme in DENV can be a drug target because it has an important role in the virus replication process. In this research, in silico simulations were carried out on bioflavonoid compounds, namely, Fisetin, Galangin, Hesperetin, Hesperidin, Myricetin, and Naringenin with Quercetin as control ligand. QSAR analysis showed that all ligand has the probability to be antiviral and RNA synthesis inhibitor. Docking scores showed that Myricetin, Hesperidin, and Fisetin show strong performance while Hesperidin, Hesperetin, and Naringenin showed strong performance in MM/GBSA. Only Hesperidin showed strong performance in both scorings. Further investigation by ADMET analysis was done to investigate toxicology and pharmacological properties. Our molecular dynamics study through RMSD showed that even though Quercetin does not give good scoring values in both docking score and MM/GBSA, it has robust stable interaction to RdRp. The strong performance of Hesperidin was also validated by protein-ligand contact fraction in 5 ns. Overall, we observed that Hesperidin shows good potential as a DENV-3-RdRp inhibitor in par with Quercetin, although further in vitro study should be conducted.

Materials selection for aluminum alloys with desired fatigue and other mechanical properties is very difficult. Usually, when fatigue properties are maximized, other mechanical properties should be compromised. In this paper, an artificial neural network was utilized to build two prediction models that has the purpose of predicting fatigue life from composition and inverse design to predict composition from fatigue properties as a tool for materials selection. A first model was built to predict fatigue life using information on alloy composition, heat treatment, and other mechanical properties. The second model is an inversion of the first model, which predicts the material compositions to get the desired fatigue performance and other mechanical properties. Both models produce good performances based on the R2 scoring metric, where the values were found to be 0.92 and 0.96 for the first and second models, respectively. This study proved that the inversion model for predicting composition based on fatigue properties can reach acceptable accuracy and can be used as a materials selection tool. In addition, to investigate how atomic properties can affect fatigue life, the third model was built. It was found that atomic properties, such as electronegativity and the radius of alloying elements, are closely related to fatigue life and can be used to predict fatigue life as well. The significance of our work is that users can design new alloys for specific applications as well as select available alloys based on fatigue property criteria.

In this work, Andrographis paniculata compounds of Andrographolide, Neoandrographolide, and 5-hydroxy-7,8,2’,3’-tetramethoxyflavone inhibition activity to SARS CoV-2 main protease were examined through in silico molecular docking and molecular dynamics simulation, with Remdesivir as control ligand. Docking score and MMGBSA were examined as well as molecular dynamics parameters: RMSD, RMSF and Protein ligand contact fraction. Our study found that Andrographis paniculata compounds of Andrographolide, Neoandrographolide, and 5-hydroxy-7,8,2’,3’-tetramethoxyflavone have comparable inhibition activity to SARS CoV-2 main protease in comparison to Remdesivir. 5-hydroxy7,8,2’,3’-tetramethoxyflavone has the lowest docking score, which was further validated by protein ligand contact fraction examination, although MMGBSA score is lowest for Remdesivir.