Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design.
Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews/mini-reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
Articles from the journal: Current Computer-Aided Drug Design; Volume 16 Issue 2:
For details on the articles, please visit this link: http://www.eurekaselect.com/node/582/current-computer-aided-drug-design/issue/16/2727/2/9757
University of Minnesota Duluth
Author(s): David M. Rajathei*, Subbiah Parthasarathy, Samuel Selvaraj.
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: http://www.eurekaselect.com/166166/article
Author(s): Suraj N. Mali*, Sudhir Sawant, Hemchandra K. Chaudhari*, Mustapha C. Mandewale
Background: Thiadiazole not only acts as “hydrogen binding domain” and “two-electron donor system” but also as constrained pharmacophore.
Methods: The maleate salt of 2-((2-hydroxy-3-((4-morpholino-1, 2,5-thiadiazol-3-yl) oxy) propyl) amino)- 2-methylpropan-1-ol (TML-Hydroxy)(4) has been synthesized. This methodology involves preparation of 4-morpholino-1, 2,5-thiadiazol-3-ol by hydroxylation of 4-(4-chloro-1, 2,5-thiadiazol-3-yl) morpholine followed by condensation with 2-(chloromethyl) oxirane to afford 4-(4-(oxiran-2-ylmethoxy)-1,2,5-thiadiazol- 3-yl) morpholine. Oxirane ring of this compound was opened by treating with 2-amino-2-methyl propan-1- ol to afford the target compound TML-Hydroxy. Structures of the synthesized compounds have been elucidated by NMR, MASS, FTIR spectroscopy.
Results: The DSC study clearly showed that the compound 4-maleate salt is crystalline in nature. In vitro antibacterial inhibition and little potential for DNA cleavage of the compound 4 were explored. We extended our study to explore the inhibition mechanism by conducting molecular docking, ADMET and molecular dynamics analysis by using Schrödinger. The molecular docking for compound 4 showed better interactions with target 3IVX with docking score of -8.508 kcal/mol with respect to standard ciprofloxacin (docking score= -3.879 kcal/mol). TML-Hydroxy was obtained in silico as non-carcinogenic and non-AMES toxic with good percent human oral absorption profile (69.639%). TML-Hydroxy showed the moderate inhibition against Mycobacteria tuberculosis with MIC 25.00 μg/mL as well as moderate inhibition against S. aureus, Bacillus sps, K. Pneumoniae and E. coli species.
Conclusion: In view of the importance of the 1,2,5-thiadiazole moiety involved, this study would pave the way for future development of more effective analogs for applications in medicinal field. For article details, please visit: http://www.eurekaselect.com/169670/article
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Author(s): Jihui Tang*, Jie Ning , Xiaoyan Liu , Baoming Wu , Rongfeng Hu *.
Journal Name: Current Computer-Aided Drug Design
Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates.
Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening.
Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%.
Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.
Rational Drug Discovery of HCV Helicase Inhibitor: Improved Docking Accuracy with Multiple Seeding in AutoDock Vina and In Si tu Minimization
Author(s): See K. Lim, Rozana Othman, Rohana Yusof, Choon H. Heh*.
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.
Current Organic Chemistry 22, Issue 2
Combinatorial Chemistry & High Throughput Screening 21, Issue 1
Current Topics in Medicinal Chemistry 18, Issue 1
Current Organic Synthesis 15, Issue 1
Current Computer-Aided Drug Design 14, Issue 1
Letters in Drug Design & Discovery 15, Issue 5