Investigation of indirubin derivatives: a combination of 3D-QSAR, molecular docking, and ADMET towards the design of new DRAK2 inhibitors

Combined computational approaches for developing new anti-Alzheimer drug candidates: 3D-QSAR, molecular docking and molecular dynamics studies of liquiritigenin derivatives

Butyrylcholinesterase is an acetylcholine-degrading enzyme involved in the memorization process, which is becoming an interesting target for the symptomatic treatment of Alzheimer's disease. In the present investigation, the structure-activity relationship of a set of Liquiritigenin derivatives recently revealed to be Butyrylcholinesterase inhibitors was studied basing on comparative field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMISA). As a result, performant models with high predictive capability have been developed (CoMFA model: R² = 0.91, Q² = 0.62, R² _pred = 0.85; CoMISA model: R² = 0.92, Q² = 0.59, R² _pred = 0.83) and implemented to design new Liquiritigenin derivatives with improved activity. Besides, the affinity of the designed derivatives towards the active site of Butyrylcholinesterase, was confirmed by molecular docking and molecular dynamics studies. Moreover, they exhibited good pharmacokinetics properties. Accordingly, the outcomes of the present investigations can provide important direction for the development of new anti-Alzheimer's drug candidates.

Cameroonian medicinal plants as potential candidates of SARS-CoV-2 inhibitors

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic instigated by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which changed the daily train of the world's population and cause several dead. Despite the significant efforts made in developing vaccines and therapeutic drugs, there is currently no available effective treatment against this new coronavirus infection, hence the need to continue research which is aimed at limiting the progression of this virus. The present study which has as objective to carry out in silico studies on the metabolites of some Cameroonian medicinal plants of the Asteraceae family with a view to propose potential molecules to fight against COVID-19. The selected plants are commonly used to treat respiratory infectious diseases, and for this reason they may contain some constituents which could exhibit an antiviral activity against SARS-CoV-2. In this work, a set of 74 naturally occurring compounds are computed with SARS-CoV-2 main protease protein (PDB ID: 6lu7) and spike protein (PDB ID: 6m0j) for their affinity and stability using binding energy analysis and molecular docking. Chrysoeriol-7-O-β-D-glucuronopyranoside (compound 16) has showed promising results including excellent Absorption, Distribution, Metabolism and Excretion (ADME) parameters as well as insignificant toxicity. Finally, the stability of this compound is complex with the two proteins validated through molecular dynamics (MD) simulation, they displayed stable trajectory and molecular properties with consistent interaction profile in molecular dynamics simulations. These findings call for further in vitro and in vivo challenges of phytoconstituents against the COVID-19 as a potential agent to fight the spread of this dramatic pandemic.Communicated by Ramaswamy H. Sarma.

QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 ​+ ​G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC₅₀ values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha's. Model 34 is chosen with higher values of R², R² _test and Q²cv (R² ​= ​0.838, R² _test ​= ​0.735, Q² _cv ​= ​0.757). It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (E_HOMO), energy of molecular orbital below HOMO energy (E_HOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium

Statistical modeling of the corrosion inhibition process by twenty-one pyridazine derivatives for mild steel in acidic medium was investigated by the quantitative structure property relationship (QSPR) approach. This modeling was established by the correlation between the corrosion inhibition efficiency (IE %) and a number of the electronic and structural properties of these inhibitors such as: the E HOMO (highest occupied molecular orbital energy), the E LUMO (lowest unoccupied molecular orbital energy), the energy gap (E L-H ), the dipole moment (μ), the hardness (η), the softness (σ), the absolute electronegativity (χ), the ionization potential (IP), the electron affinity (EA), the fraction of electrons transferred (ΔN), the electrophilicity index ω the molecular volume (V m ), the logarithm of the partition coefficient (Log P), and the molecular mass (M), in addition to the inhibitor concentration (C i ). The structure electronic properties was calculated by the use of the density functional theory method (DFT), at B3LYP/6-31G (d, p) level of theory and the analysis of dimensionality and redundancy as well as the test of collinearity between descriptors are carried out using principal component analysis (PCA). Whereas, the correlation between EI % and molecular structure is performed through the development of tree mathematical models, based-QSPR approaches: the partial least squares regression (PLS), the principal component regression (PCR) and the artificial neural networks (ANN). Indeed, the statistical quantitative results revealed that PCR and ANN were the most relevant and predictive models in comparison with the PLS model. This pertinence was demonstrated by using leave one-out cross-validation as an efficient method for testing the internal stability and predictive capability of said models with a high cross-validated determination coefficient R 2 cv = 0.92 and predicted determination coefficient R 2 pred = 0.92 and R 2 pred = 0.90 for PCR and ANN respectively; in addition to an extrapolation test set as an external validation with a significant external coefficient of determination: R 2 test = 0.94 and R 2 test = 0.92, for the two correspondingly models.

Design, synthesis, structure, in vitro cytotoxic activity evaluation and docking studies on target enzyme GSK-3β of new indirubin-3'-oxime derivatives

The addition of chalcone and amine components into indirubin-3′-oxime resulted in 15 new derivatives with high yields. Structures of new derivatives were also elucidated through 1D, 2D-NMR and HR-MS(ESI) spectra and X-ray crystallography. All designed compounds were screened for cytotoxic activity against four human cancer cell lines (HepG2, LU-1, SW480 and HL-60) and one human normal kidney cell line (HEK-293). Compound 6f exhibited the most marked cytotoxicity meanwhile cytotoxicity of compounds 6e, 6h and 6l was more profound toward cancer cell lines than toward normal cell. These new derivatives were further analyzed via molecular docking studies on GSK-3β enzyme. Docking analysis shows that most of the derivatives exhibited potential inhibition activity against GSK-3β with characteristic interacting residues in the binding site. The fast pulling of ligand scheme was then employed to refine the binding affinity and mechanism between ligands and GSK-3β enzyme. The computational results are expected to contribute to predicting enzyme target of the trial inhibitors and their possible interaction, from which the design of new cytotoxic agents could be created in the future.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.