NMD SERVER: NATURAL MEDICINES DATABASE FOR DRUG DISCOVERY
DOI:
https://doi.org/10.20319/lijhls.2017.32.216224Keywords:
Natural Drug, Target, NLP, Integration, Wrapper, Target Site, NMDdockAbstract
Cancer is the most frequently diagnosed disease globally and the second leading cause of the death. Natural Medicines are the alternative form of treatment that includes use of various plants. It is one of the safe treatment option to treat cancer and safer than allopathic medicines in order to reduce side effects. NMD Server: Natural Medicines Database for Drug Discovery is a unique and significant database of its kind, giving researchers, medical practitioners, pharmaceutical industries and students of Life Sciences an instant access to over 354 records of Natural Medicines which may be developed and used for treatment of Cancer. This database constitutes the specific information related to Natural Medicines and their respective target sites. NMD Server: Natural Medicines Database for Drug Discovery provides all the information (database fields) regarding the physiological parameters of database and is considered to be the linked table with pre-determined values and names that are included to aid in populating the fields of the linked tables. There have been many different types of fields with its respective data types that have been designated on the basis of data provided. NMDdock Tools have been integrated in this database for convenience for users like docking analysis of target and natural medicine, Sensitivity & Specificity analysis of natural medicine, Linear Correlation and Regression tool, Sequence Manipulation of target, Statistical Analysis. For the precise information about any particular drug, connectivity has been made with other databases and applications based highly bioinformatics tools have been embedded for convenience of users.
References
Bernstein FC, Koetzle TF, Williams GJ, Meyer EF, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M. The Protein Data Bank: a computer-based archival file for macromolecular structures. J. Mol. Biol. 1977; 112:535. https://doi.org/10.1016/S0022-2836(77)80200-3
Xu W and Wang R: A Novel Algorithm of Mining Multidimentional Association Rules, Lecture Notes in Control and Information Sciences. 2006; 344:2006.
Kaushik, Aman Chandra, and Vandana Sharma. Brain Tumor Segmentation from MRI images and volume calculation of Tumor. International Journal of Pharmaceutical Science Invention. 2013; 2.7: 23-26.
Bohari MH, Srivastava HK, Sastry GN. Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models. Org. Med. Chem. Lett. 2011; 1:3. https://doi.org/10.1186/2191-2858-1-3
Chin YW, Yoon KD, Kim J. Cytotoxic anticancer candidates from terrestrial plants.Anticancer Agents Med. Chem. 2009; 9:913. https://doi.org/10.2174/187152009789124664
Agrawal R., Imielinski, T., and Swami, A: Mining Association Rules between Sets of Items in Large Databases. ACM SIGMOD international. 1993; 22:2.
Ahmed J, Meinel T, Dunkel M, Murgueitio MS, Adams R, Blasse C, Eckert A, Preissner S, Preissner R Cancer Resource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge. Nucleic Acids Res. 2011; 39:960. https://doi.org/10.1093/nar/gkq910
Wolf YI, Rozogin IB, Grishin NV, Koonin EV. Genome trees and the tree of life. Trends Genet. 2002; 18:472. https://doi.org/10.1016/S0168-9525(02)02744-0
Ye H, Ye L, Kang H, Zhang D, Tao L, Tang K, Liu X, Zhu R, Liu Q, Chen YZ, et al. HIT: linking herbal active ingredients to targets. Nucleic Acids Res. 2011; 39:1055. https://doi.org/10.1093/nar/gkq1165
Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH. An overview of the PubChem BioAssay resource. Nucleic Acids Res. 2010; 38:255. https://doi.org/10.1093/nar/gkp965
Dunkel M, Fullbeck M, Neumann S, Preissner R. SuperNatural: a searchable database of available natural compounds. Nucleic Acids Res. 2006; 34:678. https://doi.org/10.1093/nar/gkj132
Dobson PD, Patel Y, Kell DB. ‘Metabolite-likeness’ as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov. Today 2009; 14:31. https://doi.org/10.1016/j.drudis.2008.10.011
Peironcely JE, Reijmers T, Coulier L, Bender A, Hankemeier T. Understanding and classifying metabolite space and metabolite-likeness. PLoS One. 2011; 6:28966. https://doi.org/10.1371/journal.pone.0028966
Ertl P, Roggo S, Schuffenhauer A. Natural product-likeness score and its application for prioritization of compound libraries. J. Chem. Inf. Model. 2008; 48:68. https://doi.org/10.1021/ci700286x
Hodge AE, Altman RB, Klein TE. The PharmGKB: integration, aggregation, and annotation of pharmacogenomic data and knowledge. Clin. Pharmacol. Ther. 2007; 81:21. https://doi.org/10.1038/sj.clpt.6100048
Newman DJ, Cragg GM. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J. Nat. Prod. 2012; 75:311. https://doi.org/10.1021/np200906s
Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc. 2000; 000 7:593.
Koehn FE, Carter GT. The evolving role of natural products in drug discovery.Nat. Rev. Drug Discov. 2005; 4:206. https://doi.org/10.1038/nrd1657
Mishra BB, Tiwari VK. Natural products: an evolving role in future drug discovery. Eur. J. Med. Chem. 2011; 46:4769. https://doi.org/10.1016/j.ejmech.2011.07.057
Csizmadia F. J. Chem: Java applets and modules supporting chemical database handling from web browsers. J. Chem. Inf. Comput. Sci. 40:323 (2000). https://doi.org/10.1021/ci9902696
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright of Published Articles
Author(s) retain the article copyright and publishing rights without any restrictions.
All published work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
