Current Computer - Aided Drug Design - Volume 17, Issue 6, 2021

Background: This study is one of the dynamics molecular docking that presents the interactions between a molecular model of the mixture of humic acid structure and 18 aristolochic acid structures, from the PubChem database in a water box that simulates the environment reactions. Objective: The major objective was to identify what happens in this procedure (LD/MM+) with the coupled species. Methods: LD/MM+ SIMULATION (Langevin dynamics simulation). Results: The R-Squared statistic indicates that the model, as fitted by MLR, explains 90.9437% of the variability in volume. Conclusion: The interactions of these acids, the types of forces, and the way that these molecules can get closer to each other, in terms of total energy density, while identifying the specificities vis-àvis of water-aromaticity or water-reactivity behaviors were observed.

Introduction: Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°vap/kJ.mol^-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. Methods: A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. Results: The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One- Out (LOO) cross-validation(Q² LOO), the five-fold cross-validation techniques, external validation parameters (Q²F1, Q²F2, Q²F3), the concordance correlation coefficient (CCC) and the predictive parameter R² m. Conclusion: The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.

Introduction: Mycobacterium tuberculosis has instigated a serious challenge toward the effective treatment of tuberculosis. The reoccurrence of the resistant strains of the disease to accessible drugs/medications has mandate for the development of more effective anti-tubercular agents with efficient activities. Time expended and costs in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multidrug resistance strain M. tuberculosis (TB). Meanwhile, to solve the stated problem, a new approach i.e. QSAR which establish connection between novel drugs with a better biological against M. tuberculosis is adopted. Methods: The anti-tubercular model established in this study to forecast the biological activities of some anti-tubercular compounds selected and to design new hypothetical drugs is subjective to the molecular descriptors; MATS7s, SM1_DzZ, SpMin4_Bhv, TDB3v and RDF70v. Ligand-receptor interactions between quinoline derivatives and the receptor (DNA gyrase) was carried out using molecular docking technique by employing the PyRx virtual screening software and discovery studio visualizer software. Furthermore, docking study indicates that compound 20 of the derivatives with promising biological activity has the utmost binding energy of -17.79 kcal/mol. Results: Meanwhile, the interaction of the standard drug; isoniazid with the target enzyme was observed with the binding energy -14.6 kcal/mol which was significantly lesser than the binding energy of the ligand (compound 20). Therefore, compound 20 served as a template structure to design compounds with more efficient activities. Among the compounds designed; compounds 20p was observed with better anti-tubercular activities with more prominent binding affinities of - 24.3kcal/mol. Conclusion: The presumption of this research aid the medicinal chemists and pharmacist to design and synthesize a novel drug candidate against tuberculosis. Moreover, in-vitro and in-vivo test could be carried out to validate the computational results.

Background: Human mitotic kinesins play a crucial role in mitotic cell division. Targeting the spindle separation phase of mitosis has gained much attention pharmaceutically in cancer chemotherapy. Spindle segregation is carried out mainly by Eg5 kinesin, and currently, it has many inhibitors in different phases of clinical trials. All the current drug candidates bind un-competitively with ATP/ADP at allosteric site 1 (formed by loop L5, helix α2 and helix α3). Recent experiments show that inhibitors that bind to the site 2 (formed by helix α4 and helix α6) are either competitive or uncompetitive to ATP/ADP. Objectives: To identify suitable lead compounds that target the mitotic kinesin Eg5, using in silico screening and their validation using in vitro and cell-based assays. Methodology: Potential inhibitors were screened for human Eg5 (kinesin-5) through structurebased virtual screening and the top-scoring compounds were validated using steady-state ATPase assay, differential scanning fluorimetry, and microscale thermophoresis. The anti-cancer activity of the compounds was evaluated in the epithelial (A549) and chronic myelogenous leukemia (K562) cancer cell lines. A known strong binding inhibitor, S-trityl-L-cystine, is used as a reference compound. Results: Out of the many compounds tested, MM01 and MM03 showed good cell-based activity against the cancer cell lines A549 and K562 and can be further studied in animal models. Conclusion: In this study, a structure-based approach was used to identify the potential inhibitors and validate them using different in-vitro and cell-based assays.

Background: Tuberculosis is a chronic infectious disease that affects one-third of the global population. The emergence of Multi-resistant (MDR) strains and high susceptibility of human immunodeficiency virus (HIV) infected persons to the disease forced to develop novel antituberculosis agents and preferably have a novel mechanism of action as to avoid cross-resistant with other agents. A literature survey indicated that Pyridine, Thiadiazole, Benzimidazole and Acetyl thiophene derivatives exhibit various pharmacological activities, including anti-mycobacterial activity. Methods: Thus, a series of Pyridine, Thiadiazole, Benzimidazole and Acetyl thiophene based molecules were designed and docked against crucial mtb enzyme target InhA (Enoyl Acyl Carrier Protein Reductase) Enzyme. The docked molecules were screened against good docking-score and multiple interactions and opted for synthesis. Synthesized molecules were recrystallized to obtain purity. All the purified compounds were characterized by various spectral analyses and evaluated for antimycobacterial activity against tuberculosis H37RV strain by Microplate Alamar Blue Assay (MABA) method. Results: The experimental results showed that Schiff bases of Pyridine (Compounds ‘d’) and Benzimidazole derivatives (Compounds ‘i’ ) possess good anti-tubercular activity with an MIC below 1.6 μg /mL. Furthermore, compound ‘e’ of benzimdazole derivative showed good antitubercular activity with an MIC below 6.25 μg /mL, whereas 2 - acetyl thiopene compounds exhibited moderate antitubercular activity below 50μg/mL. Conclusion: The comparative in vitro and molecular docking study analysis reveals that compared to chalcones of Acetyl thiophene derivatives, Pyridine, Thiadazole and Benzimidazole based Schiff bases exhibited best antitubercular activity.

Background: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. Objective: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma-Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. Methods: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using a gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand receptor interaction characteristics. Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03, respectively. Conclusion: We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

Introduction: Breast cancer is one of the leading causes of death of women every year. Estrogen receptor alpha (ER- α) is an important pathway that is responsible for the development of breast cancer. Tamoxifen is the most commonly used drug to treat breast cancer. But the main drawback of using this drug is that it increases the risk of uterine cancer, stroke, and pulmonary embolism. Methods: In this research, the in-silico approach was followed to get the anticancer agent from Withania somnifera as the root extract of the plant is active against breast cancer. For this, 15 bioactive molecules were subjected to molecular docking and 9 molecules were obtained comparing the consensus binding affinity of H3B-9224. Results: After rescoring, drug-likeness analysis and ADMET analysis of the molecules were carried out and 3 molecules remained. These 3 molecules showed good ADMET properties, which are crucial requirements in the drug discovery process. Their activity was checked by applying density functional theory (DFT) and all of them showed good reactivity. Their binding interaction was also evaluated. Conclusion: Finally, the stability of those molecules was evaluated by applying molecular dynamics (MD) simulation. After this simulation, 2 molecules remained that had good stability with the protein during the simulation period.

Background: The widespread hazardous issue of antibiotics resistance can be overcome by the development of target based potent antibacterial agents. Filamentous temperature-sensitive mutant Z (FtsZ), a simpler structural prokaryotic homolog of eukaryotic cytoskeletal tubulin, was considered as a competent target in antibacterial drug discovery. Objective: The purpose of the present work is to evaluate the antitubercular activity of virtual hits by funnel-shaped filtering with glide docking, followed by MM-GBSA binding energy and molecular dynamics simulation. Pharmacokinetics and biochemical activity of the computationally screened virtual hits have been studied to focus their potential to inhibit the bacterial cell division. Methods: The docking study was performed against the crystal structure of Staphylococcus aureus and Mycobacterium tuberculosis FtsZ protein with the hits obtained from High Throughput Virtual Screening using the Glide module in Schrodinger. ADME profile and 50 ns molecular dynamics simulation studies were performed using the Schrödinger suite. The minimal inhibitory concentration of the test compounds was determined by the colorimetric method by the Resazurin Microtiter plate Assay. Results: The binding of hit molecules T5427054 and 6M356S was mainly supported by van der Waals interaction and an electrostatic component of solvation energy computed by the MM-GBSA method. 50 ns MD simulation built stability and dynamic property of the best-docked complex T5427054/2Q1Y. Both the hit molecules displayed antimycobacterial activity with minimal inhibitory concentration 500 μg/mL. Conclusion: In this study, it is found that new screened hit molecules with better theoretical results could be preferred to use as antimycobacterial agents, and further their structural modification might be improved antimycobacterial properties of hit molecules.

Background: Drugs incorporating heterocyclic chemical skeletons possess a plethora of therapeutic activities such as anticancer, antimicrobial, antihypertensive, and antipsychiatric effects. It is becoming routine, nowadays, to use cheminformatics and bioinformatics methods to elucidate the mechanism(s) of action of such drugs.

Objective: This study aimed to probe the activity of a recently published series of N1- (anthraquinon-2-yl) amidrazone piperazine derivatives employing computational strategies[1], identify their structural basis of binding to BCR/ABL kinase domain, and explain their anticancer activities in human breast adenocarcinoma (MCF-7) and chronic myelogenous leukemia (K562) cell lines.

Methods: We applied an in silico integrative informatics approach integrating molecular descriptors, docking studies, cheminformatics, and network analysis.

Results: Our results highlighted the possible involvement of the BCR/ABL and DRD2 pathways in the anticancer activity of the studied compounds, and induced fit docking (IFD) indicated that the BCR/ABL kinase domain is a putative drug target. Additionally, high-scoring docking poses identified a unique network of hydrogen bonding with amino acids Y253, K271, E286, V299, L301, T315, M318, I360, R362, V379, and D3810.

Conclusion: Using an integrative informatics approach to characterize our anticancer compounds, we were able to explain the biological differences between synthesized and biologically validated amidrazone piperazine anticancer agents. We were also able to postulate a mechanism of action of this novel group of anticancer agents.

Background: Licorice is widely used as a hepatoprotective herb for thousands of years in Traditional Chinese Medicine, and its main chemical constituent glycyrrhizin (GL) is used as a treatment for chronic hepatitis in Japan for over 20 years. 18β-Glycyrrhetinic acid (GA) is the main active metabolite of GL. Objective: Series of GA derivatives were designed and synthesized, and their anti-HCV activities were screened to investigate the structure-activity relationship (SAR). Besides, their in-silico ADMET properties were analyzed to search for a promising lead compound for further identification of anti-HCV terpenoid candidates. Methods: GA derivatives were synthesized via reactions of oxidation, oxime, rearrangement, esterification and acylation. In vitro anti-HCV activity of derivatives was tested on the HCV cell culture (HCVcc) system. In-silico ADMET properties analysis was performed via “pkCSM” and “SwissADME” platforms. Results: Eighteen GA derivatives were synthesized, and their structures were confirmed by MS and NMR spectrums. All compounds exhibited superior HCV inhibitory activity to that of GA. Compound 2 possessed the most potent anti-HCV activity with an IC50 value of 0.79 μM, which is nearly 58 times potent than SA (a previously reported potent anti-HCV terpenoids) and >200 times than GA. SAR revealed that the introduction of 3-oxo, short-chain (C1-C3) aliphatic alcohols or cyclic aliphatic amines is conducive to improving anti-HCV activity. In-silico ADMET prediction demonstrated most of the potent compounds possessed favorable ADMET properties. Conclusion: Structural modification of GA at 3-position and 30-position is an effective approach to searching for potent anti-HCV agents. Compound 2, with the most potent anti-HCV activity and favorable in-silico ADMET properties, is a promising lead compound for further identification of anti-HCV terpenoid candidates.

Background: In recent years, the discovery and development of new drugs play a critical role in cancer therapy. Objective: In this study, the effect of MPAEA and p-acetamide on cellular toxicity and on silico in HeLa cancer cells have been investigated. Methods: In this study, 2-choloro-N-(4-methoxyphenyl)acetamide (p-acetamide) and 2-(4- methoxyphenylamino)-2-oxoethyl acrylate (MPAEA) have been synthesized and characterized by FTIR, 1H, and 13C-NMR. Cytotoxicity of p-acetamide and MPAEA have been investigated by XTT cell proliferation assay on the HeLa cell line. IC50 values of p-acetamide and MPAEA have been identified on the HeLa cell line. Further, a molecular docking study was carried out by Autodock Vina using BCL-2 (PDB ID: 4MAN), BCL-W (PDB ID: 2Y6W), MCl-1 (PDB ID: 5FDO) AKT (PDB ID: 4GV1) and BRAF (PDB ID: 5VAM) as a possible apoptotic target for anticancer activity. Results: According to the obtained results, MPAEA and p-acetamide were successfully synthesized and characterized. The interactions between ligands and anti-apoptotic proteins were evaluated by molecular docking, and their free energy of binding was calculated and used as a descriptor. Conclusion: In vitro and in silico, the results demonstrated that MPAEA had potent anticancer activity on the HeLa cell line.