3D-QSAR and virtual screening studies in identification of new Rho kinase inhibitors with different scaffolds

Synthesis, biological evaluation and in silico study of 4-(benzo[d]thiazole-2-yl) phenols based on 4-hydroxy coumarin as acetylcholinesterase inhibitors

Alzheimer's disease, characterized by cognitive decline and memory loss, is associated with decreased acetylcholine levels due to acetylcholinesterase (AChE) activity. Compounds containing a coumarin heterocyclic core coupled with thiazole exhibit excellent acetylcholinesterase inhibitory activity. In this work, we designed and synthesized a series of 4-(benzo[d]thiazole-2-yl) phenols based on 4-hydroxycoumarin. The compounds were synthesized and their inhibitory activities were evaluated through in vitro biological assays. Of the compounds investigated, 3i exhibited the strongest inhibitory activity, with an IC50 value of 2.7 µM. Molecular docking and molecular dynamics simulations were employed to elucidate the binding interactions and stability of the synthesized compounds with AChE. The results demonstrated promising inhibitory activity, suggesting potential therapeutic applications for Alzheimer's disease. This research contributes to the development of coumarin-based heterocyclic compounds as effective AChE inhibitors.

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

Predicting stability constants of transition metals; Y3+, La3+, and UO2 2+ with organic ligands using the 3D-QSPR methodology

Stability constants prediction plays a critical role in the identification and optimization of ligand design for selective complexation of metal ions in solution. Thus, it is important to assess the potential of metal-binding ligand organic in the complex formation process. However, quantitative structure-activity/property relationships (QSAR/QSPR) provide a time-and cost-efficient approach to predict the stability constants of compounds. To this end, we applied a free alignment three-dimensional QSPR technique by generating GRid-INdependent Descriptors (GRINDs) to rationalize the underlying factors effecting on stability constants of transition metals; 105 (Y³⁺), 186 (La³⁺), and 66 (UO₂ ²⁺) with diverse organic ligands in aqueous solutions at 298 K and an ionic strength of 0.1 M. Kennard- Stone algorithm was employed to split data set to a training set of 75% molecules and a test set of 25% molecules. Fractional factorial design (FFD) and genetic algorithm (GA) applied to derive the most relevant and optimal 3 D molecular descriptors. The selected descriptors using various feature selection were correlated with stability constants by partial least squares (PLS). GA-PLS models were statistically validated ( R 2  = 0.96, q² = 0.82 and R² _pred=0.81 for Y³⁺; R 2  = 0.90, q² = 0.73 and R² _pred=0.82 for La³⁺ and R 2  = 0.95, q² = 0.81 and R² _pred=0.88 for UO₂ ²⁺), and from the information derived from the graphical results confirmed that hydrogen bonding properties, shape, size, and steric effects are the main parameters influencing stability constant of metal complexation. The provided information in this research can predict the stability constant of the new organic ligand with the transition metals without experimental processes.

Exploring 3D-QSPR models of human skin permeability for a diverse dataset of chemical compounds

The control of permeation is vital not only for the topical application of lotions, creams, and ointments but also for the toxicological and risk assessment of materials from environmental and occupational hazards. To understand the effects of physicochemical properties of a variety of 211 compounds on skin permeability, we developed a three-dimensional quantitative structure-property relationship (3 D-QSPR) model. Alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs) contributed to the regression models. Kennard-Stone algorithm was employed to split data set to a training set of 159 molecules and a test set of 52 molecules. Fractional factorial design (FFD), genetic algorithm (GA) and successive projection algorithm (SPA) were applied to select the most relevant 3 D molecular descriptors. The descriptors selected using various feature selection were correlated with skin permeability constants by partial least squares (PLS) and support vector machine (SVM). SPA-SVM model gave prominent statistical values with the correlation coefficient of [Formula: see text]= 0.96, Q²= 0.73 and R²_pred=0.76. According to the analysis results, the hydrogen bonding donor and acceptor properties of the investigated compounds can influence the penetration into the human skin. Furthermore, it was found that permeability was enhanced by increasing the hydrophobicity and was diminished by increasing the molecular weight. In addition, the presence of hydrophobic groups in the target molecule, as well as their shape and position, can affect the skin permeability.

A novel proteochemometrics model for predicting the inhibition of nine carbonic anhydrase isoforms based on supervised Laplacian score and k-nearest neighbour regression

Carbonic anhydrases (CAs) are essential enzymes in biological processes. Prediction of the activity of compounds towards CA isoforms could be evaluated by computational techniques to discover a novel therapeutic inhibitor. Studies such as quantitative structure-activity relationships (QSARs), molecular docking and pharmacophore modelling have been carried out to design potent inhibitors. Unfortunately, QSAR does not consider the information of target space in the model. We successfully developed an in silico proteochemometrics model that simultaneously uses target and ligand descriptors to predict the activities of CA inhibitors. Herein, a strong predictive model was built for the prediction of protein-ligand binding affinity between nine human CA isoforms and 549 ligands. We applied descriptors obtained from the PROFEAT webserver for the proteins. Ligands were encoded by descriptors from PaDEL-Descriptor software. Supervised Laplacian score (SLS) and particle swarm optimization were used for feature selection. Models were derived using k-nearest neighbour (KNN) regression and a kernel smoother model. The predictive ability of the models was evaluated by an external validation test. Statistical results (Q²_ext = 0.7806, r²_test = 0.7811 and RMSE_test = 0.5549) showed that the model generated using SLS and KNN regression outperformed the other models. Consequently, the selectivity of compounds towards these enzymes will be predicted prior to synthesis.

Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC₅₀ values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski's rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.

Toward a hierarchical virtual screening and toxicity risk analysis for identifying novel CA XII inhibitors

Carbonic anhydrase isoform XII (CA XII) is a potential target for cancer treatment. In this study, pharmacophore modeling, hierarchical virtual screening, and toxicity risk analysis were performed for identifying novel CA XII inhibitors. A pharmacophore model of two classes of CA XII inhibitors was generated. The pharmacophore model indicated the important features of inhibitors for the binding with the CA XII. The model was then utilized to screen the ZINC and CoCoCo databases for retrieving potential hit compounds of CA XII. For accurate conclusions about the selectivity of inhibitors, the retrieved molecules which obey of Lipinski's rule of five (RO5) and have no toxicity risk were docked in a CA XII 3D structure by smina. Finally, on the basis of binding affinity and the binding mode of the molecules, twelve molecules were prioritized as promising hits. It should be noted that two of hits H5 and H6 were previously reported in the CHEMBL database. This hierarchical method is worthy of reducing the time and using almost all information available. The final hits may be used as a lead to discovery novel CA XII inhibitors.