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Virtual Screening and Its Applications in Drug Discovery Process

Virtual Screening and Its Applications in Drug Discovery Process

Gurusamy Mariappan, Anju Kumari
Copyright: © 2019 |Pages: 26
ISBN13: 9781522573265|ISBN10: 1522573267|EISBN13: 9781522573272
DOI: 10.4018/978-1-5225-7326-5.ch005
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MLA

Mariappan, Gurusamy, and Anju Kumari. "Virtual Screening and Its Applications in Drug Discovery Process." Computer Applications in Drug Discovery and Development, edited by A. Puratchikody, et al., IGI Global, 2019, pp. 101-126. https://doi.org/10.4018/978-1-5225-7326-5.ch005

APA

Mariappan, G. & Kumari, A. (2019). Virtual Screening and Its Applications in Drug Discovery Process. In A. Puratchikody, S. Prabu, & A. Umamaheswari (Eds.), Computer Applications in Drug Discovery and Development (pp. 101-126). IGI Global. https://doi.org/10.4018/978-1-5225-7326-5.ch005

Chicago

Mariappan, Gurusamy, and Anju Kumari. "Virtual Screening and Its Applications in Drug Discovery Process." In Computer Applications in Drug Discovery and Development, edited by A. Puratchikody, S. Lakshmana Prabu, and A. Umamaheswari, 101-126. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7326-5.ch005

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Abstract

Virtual screening plays an important role in the modern drug discovery process. The pharma companies invest huge amounts of money and time in drug discovery and screening. However, at the final stage of clinical trials, several molecules fail, which results in a large financial loss. To overcome this, a virtual screening tool was developed with super predictive power. The virtual screening tool is not only restricted tool small molecules but also to macromolecules such as protein, enzyme, receptors, etc. This gives an insight into structure-based and Ligand-based drug design. VS gives reliable information to direct the process of drug discovery (e.g., when the 3D image of the receptor is known, structure-based drug design is recommended). The pharmacophore-based model is advisable when the information about the receptor or any macromolecule is unknown. In this ADME, parameters such as Log P, bioavailability, and QSAR can be used as filters. This chapter shows both models with various representative examples that facilitate the scientist to use computational screening tools in modern drug discovery processes.

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