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Srimathi, R.
- In Silico Screening of Traditional Herbal Medicine Derived Chemical Constituents for Possible Potential Inhibition against SARS-CoV-2
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Authors
Affiliations
1 Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRMIST, Kattankulathur, Kancheepuram – 603203, IN
2 Department of Pharmacognosy and Phytochemistry, Parul Institute of Pharmacy & Research, Parul University, Waghodia – 391760, Gujarat, IN
3 Dr. APJ Abdul Kalam Research Lab, SRM College of Pharmacy, SRMIST, Kattankulathur, Kancheepuram – 603203, Tamil Nadu, IN
1 Department of Pharmaceutical Chemistry, SRM College of Pharmacy, SRMIST, Kattankulathur, Kancheepuram – 603203, IN
2 Department of Pharmacognosy and Phytochemistry, Parul Institute of Pharmacy & Research, Parul University, Waghodia – 391760, Gujarat, IN
3 Dr. APJ Abdul Kalam Research Lab, SRM College of Pharmacy, SRMIST, Kattankulathur, Kancheepuram – 603203, Tamil Nadu, IN
Source
Journal of Natural Remedies, Vol 20, No 2 (2020), Pagination: 79-88Abstract
The outbreak of SARS-CoV-2 has initiated an exploration to find an efficient anti-viral agent. From the previous scientific studies of traditional herbal medicines like garlic, ginger, onion, turmeric, chilli, cinchona and pepper, 131 chemical constituents were identified. The filtered search of drug-like-molecules searched using Datawarrior resulted in 13 active constituents (apoquinine, catechin, cinchonidine, cinchonine, cuprediene, epicatechin, epiprocurcumenol, epiquinine, procurcumenol, quinidine, quinine, zedoaronediol, procurcumadiol) showed no mutagenic, carcinogenic or toxic properties. In silico study of these 13 compounds with the best binding affinity towards SARS-CoV-2 protease was carried out. The ligands were subjected to molecular docking using Autodock Vina. Epicatechin and apoquine showed highest binding affinity of -7 and -7.5kcal/mol while catechin and epicatechin showed four hydrogen bond interactions. It is interesting and worth noticing the interaction of GLU166 residue with the ligand in most of the constituents. The effectiveness of catechin and epicatechin as an antiviral agent could be tested against COVID-19.Keywords
COVID-19, Catechin, Epicatechin, Data Warrior, Molecular Docking, Plant Products.References
- WHO. Naming the coronavirus disease (COVID-19) and the virus that causes it [Internet]. [cited 2020 Apr 8]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virusthat-causes-it.
- Liu RP, Ge J De, Zhong Y, Zheng Q, Sun R. Traditional Chinese medicine for treatment of COVID-19 based on literature mining of targeting cytokine storm. Chinese Traditional and Herbal Drugs. 2020; 51(5):1096–105.
- Li Y, Liu X, Guo L, Li J, Zhong D, Zhang Y, Clarke M, Jin R. Traditional Chinese herbal medicine for treating novel coronavirus (COVID-19) pneumonia: Protocol for a systematic review and meta-Analysis. Systematic Reviews. 2020; 9(1). https://doi.org/10.1186/s13643-020-01343-4. PMid:32268923 PMCid:PMC7138957
- Lerner K. SARS, MERS, and the Emergence of coronaviruses. Worldmark Global Health and Medicine Issues [Internet]. 2016. Available from:
- Yan R, Zhang Y, Li Y, Xia L, Guo Y, Zhou Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science. 2020; 367(6485):1444–8. https://doi.org/10.1126/science.abb2762. PMid:32132184. PMC id:PMC7164635.
- Ludwig S, Zarbock A. Coronaviruses and SARS-CoV2: A Brief Overview. Anesthesia and Analgesia. 2020. https://doi.org/10.1213/ANE.0000000000004845. PMid:32243297. PMCid:PMC7173023
- Wang X, Xu W, Hu G, Xia S, Sun Z, Liu Z, et al. SARS-CoV2 infects T lymphocytes through its spike proteinmediated membrane fusion. Cellular and Molecular Immunology. 2020. https://doi.org/10.1038/s41423-020-0424-9
- Wang Q, Zhang Y, Wu L, Niu S, Song C, Zhang Z, et al. Structural and functional basis of SARS-CoV-2 entry by using human ACE2. Cell. 2020. https://doi.org/10.1016/j.cell.2020.03.045. PMid:32275855 PMCid:PMC7144619
- Kaladhar SVGKD. Effects of drugs on spike glycoprotein of sars-cov 2 in control of covid-2019. International Journal of Advanced Research. 2020; 8(3):918–24. https://doi.org/10.21474/IJAR01/10706
- Law S, Leung AW, Xu C. Severe Acute Respiratory Syndrome (SARS) and Coronavirus disease-2019 (COVID-19): From causes to preventions in Hong Kong. International Journal of Infectious Diseases. 2020; 94:156–63. https://doi.org/10.1016/j.ijid.2020.03.059. PMid:32251790 PMCid:PMC7195109
- Giron CC, Laaksonen A, Silva FLB da. pbioRxiv. 2020; 2020.04.05.026377.
- Rut W, Groborz K, Zhang L, Sun X, Zmudzinski M, Hilgenfeld R, et al. Substrate specificity profiling of SARS-CoV-2 Mpro protease provides basis for anti-COVID-19 drug design. bioRxiv. 2020. https://doi.org/10.1101/2020.03.07.981928. PMid:32014497
- Rabaan AA, Al-ahmed SH, Sah R, Tiwari R, Iqbal M, Patel SK, et al. SARS-CoV-2/COVID-19 and advances in developing potential therapeutics and vaccines to counter this emerging pandemic virus - A Review. Preprints. 2020; 4:1–46. https://doi.org/10.20944/ preprints202004.0075.v1
- Bharath EN, Manjula SN, Vijaychand A. In silico drug design-tool for overcoming the innovation deficit in the drug discovery process. International Journal of Pharmacy and Pharmaceutical Sciences. 2011; 3(2):8–12.
- Ang L, Lee HW, Choi JY, Zhang J, Soo Lee M. Herbal medicine and pattern identification for treating COVID-19: A rapid review of guidelines. Integrative Medicine Research. 2020; 9(2):100407. https://doi.org/10.1016/j.imr.2020.100407. PMid:32289016 PMCid: PMC7104236
- Ferner RE, Aronson JK. Chloroquine and hydroxychloroquine in covid-19. The BMJ. 2020; 369. https://doi.org/10.1136/bmj.m1432. PMid:32269046
- Atallah P, Wagener KB, Schulz MD. ADMET: The future revealed. Macromolecules. 2013. https://doi.org/10.1002/chin.201336195
- López-López E, Naveja JJ, Medina-Franco JL. DataWarrior: An evaluation of the open-source drug discovery tool. Expert Opinion on Drug Discovery. 2019. https://doi.org/10.1080/17460441.2019.1581170.
- PMid:30806519
- Trott O, Olson AJ. Autodock vina: Improving the speed and accuracy of docking. Journal of Computational Chemistry. 2019; 31(2):455–61.
- Lindstrom W, Morris GM, Weber C, Huey R. Using AutoDock for virtual screening. The Scripps Research Institue [Internet]. 2006.
- Lin LT, Hsu WC, Lin CC. Antiviral natural products and herbal medicines. Journal of Traditional and Complementary Medicine. 2014; 4(1):24–35. https://doi.org/10.4103/2225-4110.124335. PMid:24872930 PMCid:PMC4032839
- Jin Z, Du X, Xu Y, Deng Y, Liu M, Zhao Y, et al. Structure of Mpro from COVID-19 virus and discovery of its inhibitors. Nature. 2020. https://doi.org/10.1101/2020.02.26.964882
- Lohidashan K, Rajan M, Ganesh A, Paul M, Jerin J. Pass and Swiss ADME collaborated in silico docking approach to the synthesis of certain pyrazoline spacer compounds for dihydrofolate reductase inhibition and antimalarial activity. Bangladesh Journal of Pharmacology. 2018; 13(1):23–9. https://doi.org/10.3329/bjp.v13i1.33625
- Contrera JF. Validation of Toxtree and SciQSAR in silico predictive software using a publicly available benchmark mutagenicity database and their applicability for the qualification of impurities in pharmaceuticals. Regulatory Toxicology and Pharmacology. 2013; 67(2):285–93. https://doi.org/10.1016/j.yrtph.2013.08.008. PMid: 23969001
- Analysis of Machine Learning Techniques for Breast Cancer Prediction
Abstract Views :89 |
PDF Views:0
Authors
N. Vanitha
1,
R. Srimathi
1
Affiliations
1 Department of Information Technology, N.G.P Arts and Science College Coimbatore, IN
1 Department of Information Technology, N.G.P Arts and Science College Coimbatore, IN
Source
Digital Image Processing, Vol 13, No 1 (2021), Pagination: 10-14Abstract
The most frequently happening cancer among Indian women is breast cancer, which is the second most exposed cancer in the world. Here is a chance of fifty percent for fatality in a case as one of two women diagnosed with breast cancer die in the cases of Indian women. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. [2] Breast cancer is the second most severe cancer among all of the cancers already unveiled. A machine learning technique discovers illness which helps clinical staffs in sickness analysis and offers dependable, powerful, and quick reaction just as diminishes the danger of death. In this paper, we look at five administered AI methods named Support Vector Machine (SVM), K-closest neighbours, irregular woodlands, fake/artificial neural organizations (ANNs). The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value. Furthermore, these strategies were evaluated on exactness review region under bend and beneficiary working trademark bend. At last in this paper we analysed some of different papers to find how they are predicted and what are all the techniques they were used and finally we study the complete research of machine learning techniques for breast cancer.Keywords
Breast Cancer, Prediction, Machine Learning.References
- Naveen, Dr. K Sharma (2019), Effective breast cancer prediction using ensemble machine learning model, International Conference on Recent Trends on Electronics, Information, Communication &technology.
- Ebru aydindag, Bayrak, Pinar kirchi (2019) Comparison of machine learning methods for breast cancer Diagnosis978-1 7281-1013.
- Dhanya irenic rose Perl, Sai Sindhu, Madhumathi, Siva Kumar (2019) A Comparative study of breast cancer prediction using machine learning and feature selection, conference on intelligent computing and control system, Amrita Vishwa Vidyapeetham, Amritapuri, India.
- Amarna, ikram Gagauz Meriam (2018) Breast cancer classification using machine learning LRDSI laboratory, University of Blida 1, Blida, Algeria 1.
- Tanishk Thomas, Nitesh, Pradhan, (2020), comparative analysis to predict breast cancer using machine learning algorithm, conference on inventive computational technology, Manipal university Jaipur.
- Gupta, P., and P. S. “analysis of Machine earning techniques for Breast Cancer Prediction”. International Journal of Engineering and Computer Science, Vol. 7, no. 05, May 2018, pp. 23891-5, http://www.ijecs.in/index.php/ijecs/article/view/4071.
- S. Mythili and A. V. S. Kumar, "CTCHABC- hybrid online sequential fuzzy Extreme Kernel learning method for detection of Breast Cancer with hierarchical Artificial Bee," 2015 IEEE International Advance Computing Conference (IACC), Bangalore, 2015, pp. 343-348.