Current Computer - Aided Drug Design - Volume 14, Issue 4, 2018

Background: A series of (E)-5-(4-((Z)-4-substitutedbenzylidene-2-thienylmethylene-5-oxo- 2-phenyl-4,5-dihydro-1H-imidazol-1-yl) benzylidene)thiazolidine-2,4-diones were synthesized and evaluated for antimycobacterial and antimicrobial activity. All these ligands were docked against protein (InhA) Enoyl-ACP reductase of the type II fatty acid syntase (FAS-II) system, (PDB ID: 4COD). Objective: In this report, we have designed and synthesized azole scaffolds with good antitubercular activities as there is a real need to develop new candidates with less toxicity and more efficiency toward pathogen. The obtained antimycobacterial activity data have been validated in the terms of ligand-protein interaction and were also analyzed for ADME properties to determine their potential to build up as good oral drug candidates. Methods: All the synthesized compounds have been established by elemental analysis, IR, 1H NMR, 13C NMR and Mass spectral data. In vitro antimycobacterial activity was carried out against (M. tuberculosis) H37Rv strain using Lowenstein-Jensen medium and antimicrobial activity against two gram-positive bacteria (S. aureus, S. pyogenes), two gram-negative bacteria (E. coli, P. aeruginosa) and three fungal species (C. albicans, A. niger, A. clavatus) using the broth microdilution method. In silico molecular docking studies were carried out using Glide (grid-based ligand docking) program incorporated in the Schrödinger molecular modeling package by Maestro 11.0 and ADME properties of synthesized compounds was performed using DruLito software. Results: Compounds 3a, 3b, 3d, 3g, 3i and 3n exhibited promising antimicrobial activity whereas compound 3n showed very good antimycobacterial activity along with Gilde docking score (-8.864) and with the one violation in Lipinski's rule of five. Conclusion: The wet lab result for compound 3n along with Glide XP docking score and the calculated ADME parameters give the best choice for the preparation of new derivatives in order to improve antitubercular activity in future with more improved potency.

Background: Proper validation is an important aspect of QSAR modelling. External validation is one of the widely used validation methods in QSAR where the model is built on a subset of the data and validated on the rest of the samples. However, its effectiveness for datasets with a small number of samples but a large number of predictors remains suspect. Objective: Calculating hundreds or thousands of molecular descriptors using currently available software has become the norm in QSAR research, owing to computational advances in the past few decades. Thus, for n chemical compounds and p descriptors calculated for each molecule, the typical chemometric dataset today has a high value of p but small n (i.e. n << p). Motivated by the evidence of inadequacies of external validation in estimating the true predictive capability of a statistical model in recent literature, this paper performs an extensive and comparative study of this method with several other validation techniques. Methodology: We compared four validation methods: Leave-one-out, K-fold, external and multi-split validation, using statistical models built using the LASSO regression, which simultaneously performs variable selection and modelling. We used 300 simulated datasets and one real dataset of 95 congeneric amine mutagens for this evaluation. Results: External validation metrics have high variation among different random splits of the data, hence are not recommended for predictive QSAR models. LOO has the overall best performance among all validation methods applied in our scenario. Conclusion: Results from external validation are too unstable for the datasets we analyzed. Based on our findings, we recommend using the LOO procedure for validating QSAR predictive models built on high-dimensional small-sample data.

Introduction: Nigerian medicinal plants have been demonstrated to be veritable source of lead compounds for drug discovery efforts. One such example is mangiferin. Mangiferin was originally isolated from the Nigerian plant Ceiba pentandra (Mombacaceae), after which its structure was elucidated with the aid of spectroscopy. Mangiferin, a xanthone glycoside, has also been reported in certain other plant families including Gentianaceae and Anacardiaceae. In certain other climes and different parts of the world, folkloric and traditional medicine has extensively employed Mangifera indica (another source of mangiferin) in treating different diseases. For many of such cultural uses carefully designed experimental investigations have been conducted confirming mangiferin's efficacies in those different pathologies which have included but not limited to cytotoxic as well as chemopreventive properties in selected cancer cell lines. Methods: In this study, computational techniques were employed to profile the interaction of the xanthone glycoside at the atomistic level against nine selected molecular targets with clinical relevance in tumorigenesis. In attempt to investigate the potential of the mangiferin structure as a viable starting point for synthetic exploration of mangiferin-based analogs, extensive structural modifications were performed. Results and Conclusion: By analyzing the resulting structure-energetic pattern, critical points capable of improving mangiferin interaction with the profiled targets were identified. The outcome of this study provides both direction and impetus for synthetic derivitization of the mangiferin molecule into novel optimized inhibitors for anticancer lead development.

Introduction: QSPR modelling is one of the major computational tools used to correlate molecular characteristics with physiochemical properties of molecules. In present work, QSPR models are formed using AIC and VIF multicollinearity indicators for descriptors selection taking solubility data of Paclitaxel prodrugs. Geometry optimization of these Paclitaxel prodrugs was performed at the PM6 and AM1levels using Gaussian software. Methods: Four descriptor groups such as 2D Autocorrelation, CATS_3D, WHIM, GETAWAY provided initial QSPR models with moderate accuracy for both the optimized geometry datasets. The descriptors from two descriptor-groups which were showing reasonable correlation (Q2) were combined to form improved models. The selection of descriptors was performed in multiple steps to determine optimal models which contain five and four descriptors for PM6 and AM1 optimized geometry datasets respectively. The R2 & Q2 values are 0.86 & 0.83 and 0.87 & 0.86 for PM6 and AM1 geometries respectively. Results: The models formed shows comparable results with the earlier reported results. The proposed protocol is also implemented on Huuskonen small dataset and the final QSPR model contains only two descriptors. On this smaller dataset, the QSPR model gives the R2 and Q2 values 0.87 and 0.85, respectively which is comparable to the results of Paclitaxel prodrugs. Conclusion: Our approach can be applicable to different datasets and it can assist the synthesis of molecule with better solubility. These QSPR models can be implemented for predicting the aqueous solubility of unknown Paclitaxel prodrugs.

Introduction: In this study, fifteen new 2,5-disubstituted analgouges of phthalimide were designed and synthesized using the appropriate synthetic route to evaluate anticonvulsant activity against the Maximal Electroshock (MES) and subcutaneous Pentylenetetrazole (scPTZ) compare to phenytoin as a positive control. The structures of the synthesized compounds were confirmed by FTIR, H-NMR, C-NMR and MASS spectroscopy. Methods: All the tested compounds were found to be effective in the PTZ model at dose of 60 mg/kg and most of the compounds showed protection against MES test indicative of their ability to inhibit the seizure spread at all dose ranges. Compound 3 illustrated the best efficacy among all compounds and showed more potency than phenytoin in clonic seizure and was potent as phenytoin in tonic seizure. Results & Discussion: Using a model of the Na channel, these derivatives were docked in the active site. Docking studies displayed that all synthesized compounds have more negative binding energy compare to reference drug and inhibition-constant less than phenytoin that means they can block the receptor more efficiently and usually form hydrophobic interactions or hydrogen bond interaction frequently with the domains I, II, III and rarely with domain IV.

Background: Docking and 3D QSAR studies were performed on Inhibitor of Bacterial DNA gyrase to develop a predictive Common Pharmacophore Hypothesis (CPH). Molecular interaction and binding affinities of these analogues, differing with nucleotides and amino acids are studied are performed. Methods: Biological activity is predicted by QSAR approach by relating the set of compounds. QSAR study was applied using PHASE, the docking studies were performed using Glide module and Prime/MM-GB/SA is used for free energy ligand binding calculations. Results: We obtained high pred_r2 value (pred_r2= 0. 9239), suggesting a significant external predictive ability of the QSAR model. Mutant docking score is -9. 023. In Wild type docking it shows lower binding energy and binding affinity is a decrease so, docking score of wild most dock 26 molecule is - 6.093. Protein shows various sites for molecular interaction, so from MM-GBSA. Conclusion: In the present study, development of predictive hypothesis (CPH) for 4-substituted cyclobutylphenyl quinoline derivatives and CPH were generated and results were interpreted on a developed model containing five features gives good and predictive statistical significance. Docking studies explained hydrogen bonding, pi-pi stacking with deoxy-ribonucleotide and with Ala 1120 and positive charge with deoxyribonucleotide. MM-GBSA explains the binding affinity of most docked ligand with protein.

Introduction: A novel series of multifunctional anti-Alzheimer's agents based on Nsubstituted aryl sulphonamides were designed and synthesized. During in vivo moderate to good anti- Alzheimer's Disease (AD) activity was observed as correlated by the modulation of some selected biochemical markers of AD as well as during behavioral assessment. Methods: Among the series, some compounds have shown multi-functional potency by inhibition of Acetylcholinesterase (AChE), Scopolamine induced oxidative stress and were found comparable to the standard drug. Successful modulation of biochemical markers of oxidative stress in AD, displays neuroprotective properties and did not exert any significant toxicity. Results and Conclusion: Thus, the present study has evidently shown that these series of compounds have potential to be optimized as anti-AD agents with multi-functional properties. The aryl sulphonamide nucleus might serve as a promising lead candidate for developing novel anti-AD drug.

Background: 1,6-Dihydropyrimidine exerts notable pharmacological efficiency and emerged as integral backbones for treatment of type-II diabetes mellitus. To optimize the in vitro and In-silico study we carried out on substituted 1,6-Dihydropyrimidine. The objective of the present study is to evaluate the binding interaction of 1,6-Dihydropyrimidine compounds with Protein Tyrosine Phosphatase (PTP1B) enzyme and also check ADME/T properties of best scored compounds. Methods: The In-silico study (docking) was carried out through target Protein Tyrosine Phosphatase (PTP1B) retrieved from protein data bank having PDB ID: 2QBS and the anti diabetic activity of the test compounds was tested against protein tyrosine phosphatase (PTP1B) enzyme by using Calbiochem ® PTP1B colorimetric assay kit. Results and Conclusion: The results of molecular Docking revealed that, with respect to their free binding energy 6A, 3K, 1B and 2K compounds have the lowest binding energy compared to positive control. In-silico ADME/T predictions revealed that all best scored compounds had good absorption as well as solubility characteristics through substrate binding sites. After conducting the in vitro studies it was observed that compounds having -3NO2, 3,4-OCH3, 4-NO2 and 4-Cl substitution on phenyl ring in the basic moiety shows good anti diabetic activity The present computational approach provided valuable information on the binding process of 1,6-Dihydropyrimidine compounds to the binding site of PTP-1B. These compounds may serve as potential lead compound for developing new 1,6- Dihyropyrimidine as a promising anti diabetic agent.

Background: Inhibition of penicillin binding protein 2A (PBP2A) represents a sound drug design strategy in combatting Methicillin resistant Staphylococcus aureus (MRSA). Considering the urgent need for effective antimicrobials in combatting MRSA infections, we have developed a statistically robust ensemble of molecular descriptors (1, 2, & 3-D) from compounds targeting PBP2A in vivo. Methods: 37 (training set: 26, test set: 11) PBP2A-inhibitors were submitted for descriptor generation after which an unsupervised, non-exhaustive genetic algorithm (GA) was deployed for fishing out the best descriptor subset. Assignment of descriptors to a regression model was accomplished with the Partial Least Square (PLS) algorithm. At the end, an ensemble of 30 descriptors accurately predicted the ligand bioactivity, IC50 (R = 0.9996, R2 = 0.9992, R2 a = 0.9949, SEE =, 0.2297 Q2 LOO = 0.9741). Results and Conclusion: Inferentially, we noticed that the overall efficacy of this model greatly depends on atomic polarizability and negative charge (electron) density. Besides the formula derived, the high dimensional model also offers critical insights into salient cheminformatics parameter to note during hit-to-lead PBP2A-antagonist optimization.

Introduction: In the present work, pharmacophore identification and biological activity prediction for 86 pyrazole pyridine carboxylic acid derivatives were made using the electron conformational genetic algorithm approach which was introduced as a 4D-QSAR analysis by us in recent years. In the light of the data obtained from quantum chemical calculations at HF/6-311 G** level, the Electron Conformational Matrices of Congruity (ECMC) were constructed by EMRE software. Comparing the matrices, electron conformational submatrix of activity (ECSA, Pha) was revealed that are common for these compounds within a minimum tolerance. A parameter pool was generated considering the obtained pharmacophore. Methods: To determine the theoretical biological activity of molecules and identify the best subset of variables affecting bioactivities, we used the nonlinear least square regression method and genetic algorithm. Results: The results obtained in this study are in good agreement with the experimental data presented in the literature. The model for training and test sets attained by the optimum 12 parameters gave highly satisfactory results with R2 training= 0.889, q2=0.839 and SEtraining=0.066, q2 ext1 = 0.770, q2 ext2 = 0.750, q2 ext3=0.824, ccctr = 0.941, ccctest = 0.869 and cccall = 0.927.

Background: Polypharmacology is a design or use of pharmaceutical agents in which single drug is used to treat multiple diseases. Aquaporin proteins are identified to treat migraine with aura and brain edema. This study focuses on Aquaporin-1 and Aquaporin-4. AQP-1 is expressed in small afferent sensory nerve fibers. Over-expression of peripheral nervous system causes migraine. Methods: AQP-4 is an abundant channel water protein in brain that regulates water transport to prevent homeostasis. Over-expression of AQP-4 contributes to water imbalance in ischemic pathology resulting in cerebral edema. Purpose of this study is to identify a potent inhibitor for the treatment of migraine with aura and brain edema. Results: As in the recent studies, no conventional methodologies have been focused through the approach of polypharmacology. Structures of AQP-1 and AQP- 4 proteins were retrieved from PDB (Protein Data Bank). PyRx software was used to perform molecular docking. Conclusion: Analogues of ligands were analyzed which contained significant similarities of associated proteins to get efficient inhibitor. Toxicity of lead compound was measured through admetSAR. A lead compound was predicted to treat these diseases.

Introduction: Acetylcholinesterase enzyme (AChE) is the main target in Alzheimer's disease therapy and designing of novel AChE inhibitors is a great deal of attention. Methods: In this study, 2D-QSAR and 3D-QSAR models were generated using stepwise multiple linear regressions (SW-MLR) and comparative molecular field analysis (CoMFA) respectively. Results: It was found that CoMFA model with r2 of 0.947 for the training set and r2 of 0.816 for the test set is more favorable than model which is established by SW-MLR method with r2 =0.825 and r2pred =0.778 in 2D-QSAR. Conclusion: In addition, obtaining models were validated by cross validation with cut off values of q2 > 0.5 and r2pred > 0.6.