In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material's capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure-property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors-eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Regulation of NS5B Polymerase Activity of Hepatitis C Virus by Target Specific Phytotherapeutics: An In-Silico Molecular Dynamics Approach

Chronic hepatitis caused by the hepatitis C virus (HCV) is closely linked with the advancement of liver disease. The research hypothesis suggests that the NS5B enzyme (non-structural 5B protein) of HCV plays a pivotal role in facilitating viral replication within host cells. Hence, the objective of the present investigation is to identify the binding interactions between the structurally diverse phytotherapeutics and those of the catalytic residue of the target NS5B polymerase protein. Results of our docking simulations reveal that compounds such as arjunolic acid, sesamin, arjungenin, astragalin, piperic acid, piperidine, piperine, acalyphin, adhatodine, amyrin, anisotine, apigenin, cuminaldehyde, and curcumin exhibit a maximum of three interactions with the catalytic residues (Asp 220, Asp 318, and Asp 319) present on the Hepatitis C virus NS5B polymerase of HCV. Molecular dynamic simulation, particularly focusing on the best binding lead compound, arjunolic acid (-8.78 kcal/mol), was further extensively analyzed using RMSD, RMSF, Rg, and SASA techniques. The results of the MD simulation confirm that the NS5B-arjunolic acid complex becomes increasingly stable from 20 to 100 ns. The orientation of both arjunolic acid and sofosbuvir triphosphate (standard) within the active site was investigated through DCCM, PCA, and FEL analysis, indicating highly stable interactions of the lead arjunolic acid with the catalytic region of the NS5B enzyme. The findings of our current investigation suggest that bioactive therapeutics like arjunolic acid could serve as promising candidates for limiting the NS5B polymerase activity of the hepatitis C virus, offering hope for the future of HCV treatment.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.