QSAR study and rustic ligand-based virtual screening in a search for aminooxadiazole derivatives as PIM1 inhibitors

Activity prediction, structure-based drug design, molecular docking, and pharmacokinetic studies of 1,4-dihydropyridines derivatives as α-amylase inhibitors

Objectives

Diabetes places a substantial economic burden on countries worldwide. The costs associated with diabetes management, including healthcare services, medications, monitoring equipment, and productivity losses, are substantial. The International Diabetes Federation has estimated that global healthcare expenditures associated with diabetes and its complications exceed hundreds of billions of dollars annually. Therefore, a critical need exists to develop drugs that are highly effective, affordable, and easily accessible to society.

Methods

This study explored the structural modification of 1,4-DHP derivatives to identify specific α-amylase inhibitors, with the aim of developing more effective and accessible drugs for diabetes. We evaluated the activity and binding ability of the designed compounds. In addition, we performed drug-likeness and pharmacokinetic studies on the modified compounds.

Results

Equation (1) had the highest accuracy, on the basis of internal and external assessment parameters, including R2int = 0.852, R2adj = 0.803, Q2cv = 0.731, and R2ext = 0.884. Moreover, the five potent analogs identified through structure-based drug design demonstrated a more favorable interaction than observed for the template or acarbose. Additionally, comprehensive studies on the drug-like properties and pharmacokinetics of the designed compounds supported their oral safety and favorable pharmacokinetic profiles.

Conclusions

The designed analogs show promise for developing new hypoglycemic agents. Their positive attributes and performance suggest that they may potentially serve as candidates for further research in improving treatments for high blood sugar-associated conditions.

Insights into the medicinal chemistry of heterocycles integrated with a pyrazolo[1,5-a]pyrimidine scaffold

Pyrazolo[1,5-a]pyrimidines are the dominant motif of many drugs; for instance, zaleplon and indiplon are sedative agents and ocinaplon was identified as an anxiolytic agent. The importance of this class of compounds lies in its varied and significant biological activities, and accordingly, considerable methods have been devised to prepare these compounds. Hence, other derivatives of this class of compounds were prepared by substitution reactions with different nucleophiles exploiting the activity of groups linked to the ring carbon and nitrogen atoms. The methods used vary through the condensation reactions of the aminopyrazoles with 1,2-allenic, enaminonitriles, enaminones, 1,3-diketones, unsaturated nitriles, or unsaturated ketones. Alternatively, these compounds are prepared through the reactions of acyclic reagents, as these methods were recently developed efficiently with high yields. The current review highlighted the recent progress of the therapeutic potential of pyrazolo[1,5-a]pyrimidines as antimicrobial, anticancer, antianxiety, anti-proliferative, analgesic, and antioxidant agents, carboxylesterase, translocator protein and PDE10A inhibitors, and selective kinase inhibitors.

Comparison of various methods for validity evaluation of QSAR models

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is one of the most important computational tools employed in drug discovery and development. The external validation of QSAR models is the main point to check the reliability of developed models for the prediction activity of not yet synthesized compounds. It was performed by different criteria in the literature.

METHODS

In this study, 44 reported QSAR models for biologically active compounds reported in scientific papers were collected. Various statistical parameters of external validation of a QSAR model were calculated, and the results were discussed.

RESULTS

The findings revealed that employing the coefficient of determination (r²) alone could not indicate the validity of a QSAR model. The established criteria for external validation have some advantages and disadvantages which should be considered in QSAR studies.

CONCLUSION

This study showed that these methods alone are not only enough to indicate the validity/invalidity of a QSAR model.

In silico molecular investigations of pyridine N-Oxide compounds as potential inhibitors of SARS-CoV-2: 3D QSAR, molecular docking modeling, and ADMET screening

The new coronavirus SARS-CoV-2 virus is causing a severe pneumonia in human, provoking the serious outbreak epidemic CoV-2. Since its appearance in Wuhan, China on December 2019, CoV-2 becomes the biggest challenge the world is facing today, including the discovery of antiviral drug for SARS-CoV-2. In this study, the potential inhibitory of a class of human SARS inhibitors, namely pyridine N-oxide derivatives, against CoV-2 was addressed by quantitative structure-activity relationship 3 D-QSAR. The reliable CoMSIA developed model of 110 pyridine N-oxide based-antiviral compounds, showed Q²= 0.54 and rext2=0.71. The molecular surflex-docking was applied to identify the crystal structure of CoV-2 main protease 3CLpro (PDB: 6LU7) and two potentially and largely used antiviral molecules, namely chloroquine, hydroxychloroquine. The obtained free energy affinity and ADMET properties indicate that among the series of model antiviral compounds examined, the new antiviral compound A5 could be an excellent antiviral drug inhibitor against COVID-19. The inhibition activity of pyridine N-oxyde compounds against CoV-2 was compared with the activity of two common antiviral drug, namely chloroquine (CQ) and hydroxychloroquine (HCQ). DFT method was also used to define the sites of reactivity of pyridine N-oxyde derivatives as well as CQ and HCQ.Communicated by Ramaswamy H. Sarma.

Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics and ADMET evaluation

The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected whole the world with more than 6 million confirmed cases and over 370,000 deaths. At present, there are no effective treatments or vaccine for this disease, which constitutes a serious global health crisis. As the pandemic still spreading around the globe, it is of interest to use computational methods to identify potential inhibitors for the virus. The crystallographic structures of 3CLpro (PDB: 6LU7) and RdRp (PDB 6ML7) were used in virtual screening of 50000 chemical compounds obtained from the CAS Antiviral COVID19 database using 3D-similarity search and standard molecular docking followed by ranking and selection of compounds based on their binding affinity, computational techniques for the sake of details on the binding interactions, absorption, distribution, metabolism, excretion, and toxicity prediction; we report three 4-(morpholin-4-yl)-1,3,5-triazin-2-amine derivatives; two compounds (2001083-68-5 and 2001083-69-6) with optimal binding features to the active site of the main protease and one compound (833463-19-7) with optimal binding features to the active site of the polymerase for further consideration to fight COVID-19. The structural stability and dynamics of lead compounds at the active site of 3CLpro and RdRp were examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the three complexes remain stable during simulation of 20 ns, which may be suitable candidates for further experimental analysis. As the identified leads share the same scaffold, they may serve as promising leads in the development of dual 3CLpro and RdRp inhibitors against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells

SK-MEL-5 is a human melanoma cell line that has been used in various studies to explore new therapies against melanoma in different in vitro experiments. Based on this study we report on the development of quantitative structure-activity relationship (QSAR) models able to predict the cytotoxic effect of diverse chemical compounds on this cancer cell line. The dataset of cytotoxic and inactive compounds were downloaded from the PubChem database. It contains the data for all chemical compounds for which cytotoxicity results expressed by GI₅₀ was recorded. In total 13 blocks of molecular descriptors were computed and used, after appropriate pre-processing in building QSAR models with four machine learning classifiers: Random forest (RF), gradient boosting, support vector machine and random k-nearest neighbors. Among the 186 models reported none had a positive predictive value (PPV) higher than 0.90 in both nested cross-validation and on an external dataset testing, but 7 models had a PPV higher than 0.85 in both evaluations, all seven using the RFs algorithm as a classifier, and topological descriptors, information indices, 2D-autocorrelation descriptors, P-VSA-like descriptors, and edge-adjacency descriptors as sets of features used for classification. The y-scrambling test was associated with considerably worse performance (confirming the non-random character of the models) and the applicability domain was assessed through three different methods.