While the number of AIDS-related deaths continues to rise, efforts have been made to transform the disease into a manageable chronic condition. HIV protease inhibitors have become central to combination therapy. As a result, these inhibitors have become a major focus of anti-HIV drug development. This research takes a data-driven approach to drug development through the use of quantitative structure-activity relationship (QSAR) analysis. A dataset of 450 anti-HIV drugs was used to construct and validate models. Using extensive validation methods and various machine learning algorithms, the results clearly showed that the "ET" regression outperformed the other models (“XGB”, “LGBM”, “DT”, “RF”, “GB”, “Bag”, and “HGB”) in terms of goodness of fit, predictivity, generalizability, and model robustness. Promising compounds were subjected to molecular docking and molecular dynamics simulation, resulting in drugs with favourable pharmacokinetic and pharmacodynamic properties that consistently interact with the therapeutic target.

A series of new 4-(1,3,4-thiadiazol-2-yl)-containing polysubstituted pyrroles 3 a-k has been synthesized by a preparative convenient method from ethyl 5-chloro-4-formyl-1H-pyrrole-3-carboxylates 1 a-e, which were selectively transformed into the corresponding polysubstituted pyrrole-4-carboxylic acids 2 а-е using sodium hypochlorite as an oxidizer. Further, they were transformed into the target compounds with a high yield using the cyclocondensation with N-mono- or N,N-disubstituted thiosemicarbazides in the boiling phosphorus trichloroxide. As seen from the screening of antimicrobial activity, the synthesized compounds exhibit the inhibiting and bactericide activity against some bacteria and fungi. The highest activity has been established for the compounds 3 a, c, e-h, j against the strain Klebsiella pneumoniae (МІС=31.25 µg/mL). The calculated HOMO energy level proves that the compound 3 с is the most reactive ligand for the interaction with a protein receptor. The molecular docking data show that the compound 3 h has the highest affinity to the ThiM Klebsiella pneumoniae kinase.

The pandemic COVID-19 has been spreading around the globe from December 2019 onwards and is considered the most infectious disease of this century. To date, there is no effective drug against SARS-CoV-2 discovered by pharmaceutical scientists, and the research is going rigorously all over the world. In this work, we examined the interaction of the already existing antivirals (Lopinavir, Atazanavir, and Remdesivir) with the structural proteins of SARS-CoV-2 using computational methods. Pharmacophore modeling of these drugs was conducted using molecular databases to determine the lead compounds from molecular databases. Pharmagist Webserver and Zinc Molecular Database were used to find out the pharmacophore and lead compounds, respectively. The drug-likeness properties of the compounds were evaluated by the SwissADME webserver. In silico studies showed that the binding affinities of the drugs followed the order Remdesivir > Atazanavir > Lopinavir. Docking and pharmacological studies revealed the potency and drug-likeness of the synthetic molecules.