Search for new inhibitors of MurA

What is it about?

Research for new antibacterial agents is ongoing because of the resistance of bacteria to current antibiotics. In this research, we tried to find new antibacterial agents by starting with a bioinformatic method “virtual screening” targeting MurA, and then we completed with a biological test on bacteria.

Why is it important?

As result, four compounds had antibacterial activity, one of them carried out its activity with MIC 457μg/ml against Staphylococcus aureus. Read more about the article here:

Most Accessed Articles | QSAR Analysis of Multimodal Antidepressants Vortioxetine Analogs Using Physicochemical Descriptors and MLR Modeling

Journal Name: Current Computer-Aided Drug Design

Author(s): David M. Rajathei*, Subbiah Parthasarathy, Samuel Selvaraj.



Graphical Abstract:




Background: Vortioxetine is a multimodal antidepressant drug with combined effects on SERT as an inhibitor, 5-HT1A as agonist and 5-HT3A as an antagonist. Series of vortioxetine analogs have been reported as multi antidepressant compounds and they block serotonin transport into the neuronal cells, activate the postsynaptic 5-HT1A receptors and eliminate the low activity of 5-HT3A receptors.

Objective: To explore the important properties of vortioxetine analogs involved in antidepressant activity by developing 2D QSAR models.

Methods: Selections of significant descriptors were performed by Least Absolute Shrinkage and Selection Operator (LASSO) method and, the Multiple Linear Regression (MLR) method and All Subsets and GA algorithm included in QSARINS software were used for generating QSAR models. Further, the virtual screening was performed based on bioactivity and structure similarity using the PubChem database.

Results: The four descriptor model of complementary information content (CIC2), solubility (bcutp3), mass (bcutm8) and partial charge in van der Waals surface area (PEOEVSA7) of the molecules is obtained for SERT inhibition with the significant statistics of R2= 0.69, RMSEtr= 0.44, R2 ext= 0.62 and CCCext= 0.78. For 5-HT1A agonist, the two descriptor model of molecular shape (Kappm3) and van der Waals volume of the atoms (bcutv11) with R2= 0.78, RMSEtr= 0.33, R2 ext = 0.83, and CCCext= 0.87 is established. The three descriptor model of information content (IC3), solubility (bcutp9) and electronegativity (GATSe5) of the molecules with R2= 0.61, RMSEtr= 0.34, R2 ext= 0.69 and CCCext= 0.72 is obtained for 5-HT3A antagonist. The antidepressant activities of 16 virtual screened compounds were predicted using the developed models.

Conclusion: The developed QSAR models may be useful to predict antidepressant activity for the newly synthesized vortioxetine analogs. To read out more, please visit:

EDITOR’S CHOICE ARTICLE – Parallelization of Molecular Docking: A Review

Journal Name: Current Topics in Medicinal Chemistry

Author(s): Dong Dong, Zhijian Xu, Wu Zhong, Shaoliang Peng*.



Graphical Abstract:



Molecular docking, as one of the widely used virtual screening methods, aims to predict the binding-conformations of small molecule ligands to the appropriate target binding site. Because of the computational complexity and the arrival of the big data era, molecular docking requests High- Performance Computing (HPC) to improve its performance and accuracy. We discuss, in detail, the advances in accelerating molecular docking software in parallel, based on the different common HPC platforms, respectively. Not only the existing suitable programs have been optimized and ported to HPC platforms, but also many novel parallel algorithms have been designed and implemented. This review focuses on the techniques and methods adopted in paralyzing docking software. Where appropriate, we refer readers to exemplary case studies.



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Hepatitis Day related Article

Rational Drug Discovery of HCV Helicase Inhibitor: Improved Docking Accuracy with Multiple Seeding in AutoDock Vina and In Si tu Minimization


Journal Name: Current Computer-Aided Drug Design

Author(s): See K. Lim, Rozana Othman, Rohana Yusof, Choon H. Heh*.


Graphical Abstract:



Background: Hepatitis C is a significant cause for end-stage liver diseases and liver transplantation which affects approximately 3% of the global populations. Despite the current several direct antiviral agents in the treatment of Hepatitis C, the standard treatment for HCV infection is accompanied by several drawbacks, such as adverse side effects, high pricing of medications and the rapid emerging rate of resistant HCV variants.

Objectives: To discover potential inhibitors for HCV helicase through an optimized in silico approach.Methods: In this study, a homology model (HCV Genotype 3 helicase) was used as the target and screened through a benzopyran-based virtual library. Multiple-seedings of AutoDock Vina and in situ minimization were to account for the non-deterministic nature of AutoDock Vina search algorithm and binding site flexibility, respectively. ADME/T and interaction analyses were also done on the top hits via FAFDRUG3 web server and Discovery Studio 4.5.Results: This study involved the development of an improved flow for virtual screening via implemention of multiple-seeding screening approach and in situ minimization. With the new docking protocol, the redocked standards have shown better RMSD value in reference to their native conformations. Ten benzopyran-like compounds with satisfactory physicochemical properties were discovered to be potential inhibitors of HCV helicase. ZINC38649350 was identified as the most potential inhibitor.

Conclusion: Ten potential HCV helicase inhibitors were discovered via a new docking optimization protocol with better docking accuracy. These findings could contribute to the discovery of novel HCV antivirals and serve as an alternative approach of in silico rational drug discovery.
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Highlighted article from the journal Combinatorial Chemistry & High Throughput Screening


Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery

Abstract: Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

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Major Article Contributions by Some of our Indian Authors in Bentham Science Publishers Journal: Medicinal Chemistry


New Quinolinyl–1,3,4–Oxadiazoles: Synthesis, In Vitro Antibacterial, Antifungal and Antituberculosis Studies

Author(s): Rahul V. Patel, Premlata Kumari and Kishor H. Chikhalia

Affiliation: Department of Applied Chemistry, S. V. National Institute of Technology, Surat–395007, Gujarat, India.


In order to generate hybrid antimicrobial remedies with novel mode of action, two series of quinoline based 1,3,4-oxadiazole derivatives condensed with N-aryl/benzothiazolyl acetamides were synthesized and the MIC values of the compounds towards eight reference bacterial strains (S. aureus, B. cereus, E. coli, P. aeruginosa, K. pneumoniae, S. typhi, P. vulgaris, S. flexneri), four fungi (A. niger, A. fumigatus, A. clavatus, C. albicans) and Mycobacterium tuberculosis H37Rv were assayed in vitro. Quinoline–6–carboxlic acid was treated with thionyl chloride in refluxing methanol to obtain the corresponding ester derivative to be hydrazinolyzed by 99% hydrazine hydrate in ethanol to produce carbohydrazide intermediate. The carbohydrazide precursor underwent cyclization by carbon disulfide and ethanolic KOH to construct 5–quinolinyl–6–yl–1,3,4–oxadiazol–2–thiol. Substituted 2–chloro–N–phenyl(benzothiazolyl)aceta-mide derivatives were then condensed to 1,3,4-oxadiazole nucleus via sulphur linkage to yield the desired products. Target products bearing N–benzothiazolyl–2–chloroacetamides displayed good inhibitory potential. The biological screening identified that many final analogues exhibited a significant inhibition of the growth of microorganisms at 3.12-25 μg/mL of MIC, which were comparable to control drugs. The influence of the presence of various functional groups to the phenyl/benzothiazolyl ring on activity profiles was investigated. The proposed structures of the newly prepared products were confirmed with the aid of IR, 1H NMR, 13C NMR spectroscopy and elemental analysis. These results may provide new insights in the design of a novel pool of bioactive templates.

A Combination of 3D-QSAR Modeling and Molecular Docking Approach for the Discovery of Potential HIF Prolyl Hydroxylase Inhibitors

Author(s): Mahesh Kumar Teli and Rajanikant Golgodu Krishnamurthy

Affiliation: School of Biotechnology Coordinator, Bioinformatics Centre National Institute of Technology Calicut Calicut – 673601, Kerala, India.


Suppression of HIF prolyl hydroxylase (PHD) activity by small molecule inhibitors leads to the stabilization of HIF and offers a potential therapeutic option for treating ischemic disorders. In this study, pharmacophore based QSAR modeling, virtual screening and molecular docking approaches were concurrently used to identify target-specific PHD inhibitors with better ADME properties and to readily minimize false positives and false negatives. A 3D-QSAR based method was used to generate a pharmacophore hypothesis (AAAN). The obtained 3D-QSAR model has an excellent correlation coefficient value (r2 = 0.99), Fisher ratio (F = 386) and exhibited good predictive power (q2 = 0.64). The hypothesis was validated and utilized for chemical database screening and the retrieved compounds were subjected to molecular docking for further refinement. Quantitative AAAN hypothesis comprised three H-bond accepter and one negative ionizable group feature and it give good predictive ability because all the QSAR information it was providing matched with the active site information. The hypothesis was validated and used as a 3D query for database screening. After manual selection, molecular docking and further refinement, based on the molecular interactions of inhibitors with the essential amino acids residues, 12 candidates with good ADME and blood brain barrier permeability values were selected as potential PHD inhibitors.

Modeling of LIM-Kinase 2 Inhibitory Activity of Pyrrolopyrimidine Analogues: Useful in Treatment of Ocular Hypertension and Glaucoma

Author(s): Gagandip Singh, Manish K. Gupta, Viney Kumar and Yenamandra S. Prabhakar

Affiliation: Molecular Modeling and Pharmacoinformatics Lab, Department of Pharmaceutical Chemistry, ISF College of Pharmacy, Moga-142001, India.


The LIM-Kinase 2 (LIMK2) inhibitory activity of a series of pyrrolopyrimidine analogs has been analyzed through combinatorial protocol in multiple linear regressions (CP-MLR) and partial least square (PLS) using different descriptors obtained from DRAGON software. The empirical, topological and charge descriptors have led to statistically significant QSAR models and showed good external predictivity as reflected in test set R2 values (0.782 to 0.888). The obtained structure-activity correlations underlined the significance of bulkiness and molecular polarizability in improving the activity. The topological descriptors suggested that open chain or branched substituents are favorable while cyclic /ring substituents are unfavorable for the activity. The descriptors identified in the study showed that pyrrolopyrimidine scaffold holds scope for modulating LIMK2 inhibitory activity. The study gives a direction for further exploration of chemical space of pyrrolopyrimidine analogs as LIMK2 inhibitors.

Variable Selection Based QSAR Modeling on Bisphenylbenzimidazole as Inhibitor of HIV-1 Reverse Transcriptase

Author(s): Surendra Kumar and Meena Tiwari

Affiliation: Computer Aided Drug Design Lab, Department of Pharmacy, Shri G. S. Institute of Technology and Science, 23, Park Road, Indore-452003, (M.P.), India.


The emergence of mutant virus in drug therapy for HIV-1 infection has steadily risen in the last decade. Inhibition of reverse transcriptase enzyme has emerged as a novel target for the treatment of HIV infection. The aim to decipher the structural features that interact with receptor, we report a quantitative structure activity relationship (QSAR) study on a dataset of thirty seven compounds belonging to bisphenylbenzimidazoles (BPBIs) as reverse transcriptase inhibitors using enhanced replacement method (ERM), stepwise multiple linear regression (Stepwise-MLR) and genetic function approximation (GFA) method for selecting a subset of relevant descriptors, developing the best multiple linear regression model and defining the QSAR model applicability domain boundaries. The enhanced replacement method was found to give better results r2=0.8542, Q2(loo) = 0.7917, r2pred = 0.7812) at five variables as compared to stepwise MLR and GFA method, evidenced by internal and external validation parameters. The modified r2 (r2m) of the training set, test set and whole data set were calculated and are in agreement with the enhanced replacement method. The results of QSAR study rationalize the structural requirement for optimum binding of ligands. The developed QSAR model shows that hydrophobicity, flexibility, three dimensional surface area, volume and shape of molecule are important parameters to be considered for designing new compounds and to decipher reverse transcriptase enzyme inhibition activity of these compounds at molecular level. The applicability domain was defined to find the similar analogs with better prediction power.

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