Dengue fever is a disease transmitted by infected mosquitoes. This disease spreads in several countries, especially those with a tropical climate. To date, there is no specific drug that can be used to treat dengue. Use of clinically investigated drugs, such as Balapiravir, is still not effective in inhibiting the activity of virus replication. The design of a drug candidate can be performed by using the non-structural protein 3 (NS3) as target. This study aimed to develop QSAR models to predict the inhibitory activity class of NS3 inhibitors. The classification was performed by using feature importance analysis for selecting the descriptors and three ensemble methods, i.e. random forest (RF), adaptive boosting (AdaBoost), and extremely randomized trees (ERT), for model design and prediction. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that model 9, developed from ERT produced the best performance with values of accuracy and AUC equal to 0.73 and 0.82, respectively. Use of y-scrambling method allowed us to confirm that the model was not related to the chance correlation.

We investigate thiazolyl-pyrazoline derivatives as promising drugs for the anti-malarial. Protein kinase G is a primary target for treating malaria due to its essential role in Plasmodium falciparum life cycle. In this present study, several computational approaches such as QSAR modelling, molecular docking, and all-atom MD simulation are performed to screen 36 drug candidates against malaria. From QSAR analysis, three potent drugs are selected based on the strong correlation between the inhibitory action, i.e. pEC50 and various descriptors. Further, those selected drugs are used as ligand molecules for molecular docking. We predict three complexes of models 1, 2, and 3 bind to the catalytic site of protein kinase G, suggesting those ligands may become potent inhibitors for Plasmodium falciparum. To validate the structural stability of those complexes, the parameters of RMSD, RMSF, and Rg are calculated from MD simulation. All models are stable along simulation since no significant fluctuations are observed in those validity parameters. Moreover, the binding energy is estimated at each model using MM-GBSA method and model 2 becomes the most stable structure. Finally, three ligands are assumed to have potential as inhibitors and the ligand of model 2 may become the most promising drug against malaria.

SARS-CoV-2 PLpro was investigated as a therapeutic target for potent antiviral drugs due to its essential role in not only viral replication but also in regulating the inborn immune response. Several computational approaches, including homology modelling, molecular docking, and molecular dynamics (MD) studies, were employed to search for promising drugs in treating SARS-CoV-2. Eighty-one compounds, sub-structurally similar to the antiviral drug, were used as potential inhibitors of PLpro. From our results, three complexes containing the ligands with Pubchem IDs: 153012995, 12149203, and 123608715 showed lower binding energies than the control (Ritonavir), indicating that they may become promising inhibitors for PLpro. MD was performed in a water solvent to validate the stability of the three complexes. All complexes achieved stable structure during the simulation as no significant fluctuations were observed in the validation parameters. Moreover, the binding energy for each complex was estimated using the MM-GBSA method. Complex 1 was the most stable structure based on the lowest binding energy score and its structure remained in a similar cavity with the docket snapshot. Based on our studies, three ligands were assumed to be potential inhibitors. The ligand of complex 1 may become the most promising antiviral drug against SARS-CoV-2 targeting PLpro.

Type-2 diabetes mellitus is an epidemic disease that is characterized by the chronic increase of glucose level. The insulin hormone is known to correspond to this disease, while PTP1B involved in the regulation of this hormone. Hence, PTP1B has become the primary target of drug development to treat this disease. In this study, we aim to develop QSAR model to predict PTP1B inhibitor by using a neural network method. Genetic algorithm (GA) method was used to select the set of the molecular descriptor. We improved the performance of the models by performing a hyperparameter tuning procedure. From the results of validation analysis, we found that the model 2 containing 5 descriptors as the best model. This confirms by the value of MCC (0.68) and AUC (0.89) of this model that is higher than the others. Also, the additional y-scrambled analysis confirms that this model does not correspond to coincidental correlation, indicated by a very low value of MCC of the scrambled model.