Drug discovery and development is a very expensive and time-consuming process. To improve the efficiency of the pipeline, Computer-Aided Drug Design (CADD) has become increasingly important over the years. CADD represents an interplay paradigm between in-depth in silico studies and the follow-up wet-lab validations. However we have not achieved true synergy among various computational subdisciplines, such as cheminformatics and bioinformatics. This book, therefore, focuses on the design, development and applications of novel approaches for CADD and protein structure bioinformatics. These approaches were developed in the areas of both ligand-based and structure-based drug design. In addition, Simplicial Neighborhood Analysis of Protein Packing (SNAPP) has been employed for the analysis of protein flexibility. This has resulted in powerful and cutting-edge computational tools for the rational drug design and the prediction of unintuitive consequences of targeted therapeutics. The work should help shed some light on this exciting field, and should be especially useful to professionals in computational drug discovery and other biomedical research-related areas.
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Drug discovery and development is a very expensive and time-consuming process. To improve the efficiency of the pipeline, Computer-Aided Drug Design (CADD) has become increasingly important over the years. CADD represents an interplay paradigm between in-depth in silico studies and the follow-up wet-lab validations. However we have not achieved true synergy among various computational subdisciplines, such as cheminformatics and bioinformatics. This book, therefore, focuses on the design, development and applications of novel approaches for CADD and protein structure bioinformatics. These approaches were developed in the areas of both ligand-based and structure-based drug design. In addition, Simplicial Neighborhood Analysis of Protein Packing (SNAPP) has been employed for the analysis of protein flexibility. This has resulted in powerful and cutting-edge computational tools for the rational drug design and the prediction of unintuitive consequences of targeted therapeutics. The work should help shed some light on this exciting field, and should be especially useful to professionals in computational drug discovery and other biomedical research-related areas.
Shuxing Zhang, Ph.D.: Studied Computational Drug Discovery and Medicinal Chemistry at the University of North Carolina at Chapel Hill. Currently Assistant Professor in the Department of Experimental Therapeutics and Director of Molecular Modeling Core Facility at M.D. Anderson Cancer Center, Houston, USA.
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