Virtual Screening and Its Applications in Drug Discovery Process

Virtual Screening and Its Applications in Drug Discovery Process

Gurusamy Mariappan (St. Mary's College of Pharmacy, India) and Anju Kumari (St. Mary's College of Pharmacy, India)
Copyright: © 2019 |Pages: 26
DOI: 10.4018/978-1-5225-7326-5.ch005

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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Virtual Drug Screening

A pharmacophore is an abstract description of molecular features that are necessary for molecular recognition of a ligand by biological macromolecules. Gund proposed the pharmacophores could be used to search the database based on similarity in structure (Gund P, 1977). It paves the way to develop and apply 3D databases pharmacophore to discover novel leads (Kurogi Y & Guner OF, 2001; Langer T & Hoffmann RD, 2001). There are two steps in pharmacophore-based screening: one is identification of pharmacophore model and the second step is 3-D search based on the specific constraints. The most benefit of VS is that it can be applied to large databases.

Pharmacophore models are generally used when the active lead is identified, but the 3D image of the target is unknown. The active molecules are considered as training set and used for pharmacophore identification which is atoms/groups interact with the receptor.

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