01 Pages : 1-8
http://dx.doi.org/10.31703/gdddr.2017(II-I).01 10.31703/gdddr.2017(II-I).01 Published : Dec 2017Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction
In modern day, Data on different diseases and drug substances with their properties like modification, side effects, and dose requires documentation data and building library exploring, such library with vast information in every aspect needs computational methods used in CADD. Recognition of specific targets for the drug tested and defining pharmacological activity of a drug candidate based on the structure of both drug and its target, finding outside effects of drugs at the molecular level and calculation of toxicity caused by metabolism of drug applications of Computer aided drug design in the drug discovery process. We can get additional tools and websites which serve As a tool for the source of data and computational drug design are available on the web interface and being used extensively by researchers and scientists to save time and budget for speeding up the process of experiments for Novel Drug compound.
-
Computer Aided Drug Design, Pharmacokinetics, Computational Methods, Drug Discovery, Novel Drug Candidate, Target -Drug Interaction
-
(1) Arif Paiman
Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
(2) Ahmad Mohammad
Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
(3) Mubashar Rehman
Assistant Professor, Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
- Akoka, J., Comyn-Wattiau, I., & Laoufi, N. (2017). Research on big data-a systematic mapping study. Computer Standards & Interfaces, 54, 105-115.
- Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2016). Accurate calculation of the absolute free energy of binding for drug molecules. Chemical Science, 7(1), 207-21
- Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006). Combining docking and molecular dynamic simulations in drug design. Medicinal Research Reviews, 26(5), 531-568
- Alpaydin, E. (2004). Introduction to machine learning (adaptive computation and machine learning). Cambridge, MA: The MIT Press.
- Andrade, C. H., Neves, B. J., Melo-Filho, C. C., Rodrigues, J., Silva, D. C., Braga, R. C., & Cravo, P. V. L. (2018). In silico chemogenomics drug repositioning strategies for neglected tropical diseases. Current Medicinal Chemistry. [in press]. https:// dx.doi.org/10.2174/0929867325666180309114824
- Arcon, J. P., Defelipe, L. A., Modenutti, C. P., Lo ÃŒÂpez, E. D., Alvarez-Garcia, D., Barril, X., ... Mart ÃŒÂı, M. A. (2017). Molecular dynamics in mixed solvents reveals protein-ligand interactions, improves docking, and allows accurate binding free energy predictions. Journal of Chemical Information and Modeling, 57, 846-863.
- Atkinson, S. J., Soden, P. E., Angell, D. C., Bantscheff, M., Chung, C., Giblin, K. A., ... Prinjha, R. K. (2014). The struc- ture based design of dual HDAC/BET inhibitors as novel epigenetic probes. Medicinal Chemical Communications, 5, 342-351.
- Bajorath, J. (2013). A perspective on computational chemogenomics. Molecular Informatics, 32, 1025-1028. Bajorath, J. (2017). Molecular similarity concepts for informatics applications: (pp. 231-245). New York, NY: Humana Press. https://dx.doi.org/10.1007/978-1-4939-6613-4_13.
- Barmania, F., & Pepper, M. S. (2013). C-C chemokine receptor type five (CCR5): an emerging target for the control of HIV infection. Applied & Translational Genomics, 2, 3-16.
- Beauchamp, K. A., Lin, Y.-S., Das, R., & Pande, V. S. (2012). Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements. Journal of Chemical Theory and Computation, 8, 1409-1414.
- Beisken, S., Meinl, T., Wiswedel, B., de Figueiredo, L. F., Berthold, M., & Steinbeck, C. (2013). KNIME-CDK: workflow-driven cheminformatics. BMC Bioinformatics, 14, 257
- Best, R. B., Buchete, N.-V., & Hummer, G. (2008). Are current molecular dynamics force fields too helical? Biophysical Journal, 95, L07-L09.
- Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? The Journal of Arthroplasty, 33, 2358-2361.
- Bornmann, L. (2012). Measuring the societal impact of research. EMBO Reports, 13, 673-676.
- Brindha, S., Vincent, S., Velmurugan, D., Ananthakrishnan, D., Sundaramurthi, J. C., & Gnanadoss, J. J. (2017). Bioinformatics approach to prioritize known drugs towards repurposing for tuberculosis. Medical Hypotheses, 103, 39-45.
- Computer Aided Drug Design: from Discovery of Novel Pharmaceutical Agents to Systems Pharmacology
- February 17, 2020, V.V. Poroikov, Institute of Biomedical Chemistry, ul. Pogodinskaya 10, bldg. 8, Moscow, 119121 Russia pages (222-223).
- Saldıvar-Gonza ÃŒÂlez, F. I., Prieto-Mart ÃŒÂınez, F. D., & Medina-Franco, J.L. (2017). Descubrimiento y desarrollo de
- Wassermann, A. M., Lounkine, E., Hoepfner, D., Le Goff, G., King, F. J., Studer, C., ... Glick, M. (2015). Dark chemical matter as a promising starting point for drug lead discovery. Nature Chemical Biology, 11, 958-966.
- WHO. (2018). World Health Organization. Retrieved from Accessed 24 March 2018. http://www.who.int/en/.
- Yadav, D. K., Kumar, S., Saloni, Singh, H., Kim, M. H., Sharma, P.,
- Yang, H., Sun, L., Li, W., Liu, G., & Tang, Y. (2018). In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Frontiers in Chemistry, 6, 30.
Cite this article
-
APA : Paiman, A., Mohammad, A., & Rehman, M. (2017). Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction. Global Drug Design & Development Review, II(I), 1-8. https://doi.org/10.31703/gdddr.2017(II-I).01
-
CHICAGO : Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. 2017. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review, II (I): 1-8 doi: 10.31703/gdddr.2017(II-I).01
-
HARVARD : PAIMAN, A., MOHAMMAD, A. & REHMAN, M. 2017. Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction. Global Drug Design & Development Review, II, 1-8.
-
MHRA : Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. 2017. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review, II: 1-8
-
MLA : Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review, II.I (2017): 1-8 Print.
-
OXFORD : Paiman, Arif, Mohammad, Ahmad, and Rehman, Mubashar (2017), "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction", Global Drug Design & Development Review, II (I), 1-8
-
TURABIAN : Paiman, Arif, Ahmad Mohammad, and Mubashar Rehman. "Role of Computer Aided Drug Design in Modern Drug Discovery and Pharmacokinetic Prediction." Global Drug Design & Development Review II, no. I (2017): 1-8. https://doi.org/10.31703/gdddr.2017(II-I).01