Drug Design and Discovery: Methods and Protocols by Yat T. Tang, Garland R. Marshall (auth.), Seetharama D.

By Yat T. Tang, Garland R. Marshall (auth.), Seetharama D. Satyanarayanajois (eds.)

Research within the pharmaceutical sciences and medicinal chemistry has taken an immense new path long ago twenty years with a spotlight on huge molecules, particularly peptides and proteins, in addition to DNA therapeutics. In Drug layout and Discovery: tools and Protocols, top specialists supply an in-depth view of key protocols which are time-honored in drug discovery laboratories. overlaying either vintage and state of the art innovations, this quantity explores computational docking, quantitative structure-activity dating (QSAR), peptide synthesis, labeling of peptides and proteins with fluorescent labels, DNA-microarray, zebrafish version for drug screening, and different analytical screening and organic assays which are oftentimes used in the course of the drug discovery strategy. Written within the hugely winning Methods in Molecular Biology™ sequence layout, chapters comprise introductions to their respective subject matters, lists of the mandatory fabrics, step by step, with no trouble reproducible laboratory protocols, and pointers on troubleshooting and keeping off recognized pitfalls.

Thorough and obtainable, Drug layout and Discovery: equipment and Protocols function an important laboratory reference for pharmaceutical chemists, medicinal chemists, and pharmacologists in addition to for molecular biologists.

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If the target structures are known, structure-based approaches can be used such as molecular docking, which employs the target 3D structures to design novel active compounds with improved potency. As more structures are becoming available, the prediction accuracy will likely to be improved. 1. Ligand-Based Drug Design In the absence of the receptor 3D information, lead identification and optimization depend on available pharmacologically relevant agents and their bioactivities (5–8). The computational approaches include QSAR, pharmacophore modeling, and database mining (8–10).

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