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The book summarizes important aspects of cheminformatics that are relevant for natural product research. It highlights cheminformatics tools that help to match natural products with their respective biological targets or off-targets, and discusses the potential and limitations of this approach.
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Medicinal and aromatic plants are beneficial to human health. Plant-derived molecules possess biological activities that can be used to prevent many infectious diseases and metabolic disorders. Ethnobotany and Ethnopharmacology of Medicinal and Aromatic Plants summarizes techniques and methods used to study the biological activities of plant-derived extracts and compounds to study ethnobotanical and ethnopharmacological features of medicinal and aromatic plants. This book: Includes computational approaches to study the pharmacological properties of biomolecules in medicinal and aromatic plants. Details methods in ethnopharmacology including chromatographical and analytical techniques. Demons...
A major source of active compounds, natural products from different sources supply a large variety of molecules that have been approved for clinical use or used as the starting points of optimization programs. This book features nine papers (eight full articles and one review paper) written by more than 45 scientists from around the world. These papers illustrate the development and application of a broad range of computational and experimental techniques applied to natural product research. On behalf of the contributors to the book, our hope is that the research presented here contributes to advancements in the field, and encourages multidisciplinary teams, young scientists, and students to further advance in the discovery of pharmacologically-active natural compounds
Vol. 2 of Chemoinformatics of Natural Products introduces the reader to the currently available tools for toxicity prediction, drug property prediction, an enumeration of compounds, scaffolds and functional groups in nature, computational methods for lead identification, metabolite biosynthesis, etc. Selected case studies and hands-on tutorial exercises have been included.
In the current drug research environment in academia and industry, cheminformatics and virtual screening methods are well established and integrated tools. Computational tools are used to predict a compound’s 3D structure, the 3D structure and function of a pharmacological target, ligand-target interactions, binding energies, and other factors essential for a successful drug. This includes molecular properties such as solubility, logP value, susceptibility to metabolism, cell permeation, blood brain barrier permeation, interaction with drug transporters and potential off-target effects. Given that approximately 40 million unique compounds are readily available for purchase, such computational modeling and filtering tools are essential to support the drug discovery and development process. The aim of all these calculations is to focus experimental efforts on the most promising candidates and exclude problematic compounds early in the project. In this Research Topic on virtual activity predictions, we cover several aspects of this research area such as historical perspectives, data sources, ligand treatment, virtual screening methods, hit list handling and filtering.
Drug discovery is all about finding small molecules that interact in a desired way with larger molecules, namely proteins and other macromolecules in the human body. If the three-dimensional structures of both the small and large molecule are known, their interaction can be tested by computer simulation with a reasonable degree of accuracy. Alternatively, if active ligands are already available, molecular similarity searches can be used to find new molecules. This virtual screening can even be applied to compounds that have yet to be synthesized, as opposed to "real" screening that requires cost- and labor-intensive laboratory testing with previously synthesized drug compounds. Unique in its...
Chemoinformatics is broadly a scientific discipline encompassing the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information. It is distinct from other computational molecular modeling approaches in that it uses unique representations of chemical structures in the form of multiple chemical descriptors; has its own metrics for defining similarity and diversity of chemical compound libraries; and applies a wide array of statistical, data mining and machine learning techniques to very large collections of chemical compounds in order to establish robust relationships between chemical structure and its physical or biological pr...
The first edition of Bioactive Compounds from Natural Sources was published in a period of renewed attention to biologically active compounds of natural origin. This trend has continued and intensified-natural products are again under the spotlight, in particular for their possible pharmacological applications. Largely focusing on natural products
This handbook is the first to address the practical aspects of this novel method. It provides a complete overview of the field and progresses from general considerations to real life scenarios in drug discovery research. Starting with an introductory historical overview, the authors move on to discuss ligand-based approaches, including 3D pharmacophores and 4D QSAR, as well as the concept and application of pseudoreceptors. The next section on structure-based approaches includes pharmcophores from ligand-protein complexes, FLIP and 3D protein-ligand binding interactions. The whole is rounded off with a complete section devoted to applications and examples, including modeling of ADME properties. With its critical evaluation of pharmacophore-based strategies, this book represents a valuable aid for project leaders and decision-makers in the pharmaceutical industry, as well as pharmacologists, and medicinal and chemists.