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Quantitative Structure-Activity Relationship --- Drug Resistance, Microbial --- Endocrine Disruptors

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For thousands of years, pharmacological knowledge coming from natural remedies, has been handed down from generation to generation, without any awareness of the ways in which preparations are made to face diseases. The advent of pharmaceutical chemistry and of the modern drug industry turned that lack of awareness into a scientific knowledge that changed the destiny of the human race. The twenty-eight chapters of this book, are taken from the lectures held by Professor Ettore Novellino every year in his course “Pharmaceutical Chemistry and Toxicology 2”. The first chapters address the basic notions of drugs, homeostasis, pharmacopoeia, and receptor; then, the different pharmaceutical classes are introduced by analyzing their pharmacological and chemical aspects. In particular, the structural study of the interaction between drugs and receptors or biological enzymes gives the fundamentals to connect the chemical and stereochemical properties of a compound family, with the biological activity, a correlation better known as Quantitative Structure-Activity Relationship (QSAR). Several examples of the synthesis of some of the most historically renown drugs, provided at the end of each chapter, integrate the book.

Metabotropic receptor --- Pharmaceutical Chemistry --- Quantitative Structure-Activity Relationship

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Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates.

Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Drugs --- Drug development. --- QSAR (Biochemistry) --- Cheminformatics. --- Machine learning --- Quantitative Structure-Activity Relationship --- Cheminformatics --- Machine Learning --- Drug Design --- Structure-activity relationships. --- Therapeutic use.

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Oral health is general health. If the oral cavity is kept healthy, the whole body is always healthy. Bacteria in the oral cavity do not stay in the oral cavity, but rather they travel throughout the body and can induce various diseases. Periodontal pathogens are involved in tooth loss. The number of remaining teeth decreases with age. People with more residual teeth can bite food well and live longer with lower incidence of dementia. There are many viruses in the oral cavity that also cause various diseases. Bacteria and viruses induce and aggravate inflammation, and therefore should be removed from the oral cavity. In the natural world, there are are many as yet undiscovered antiviral, antibacterial and anti-inflammatory substances. These natural substances, as well as chemically modified derivatives, help our oral health and lead us to more fulfilling, high quality lives. This Special Issue, entitled “Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions”, was written by specialists from a diverse variety of fields. It serves to provide readers with up-to-date information on incidence rates in each age group, etiology and treatment of stomatitis, and to investigate the application of such treatments as oral care and cosmetic materials.

gargle --- oral lichen planus --- angiotensin II blocker --- quantitative structure-activity relationship --- metabolomics --- CCN2 --- anti-human immunodeficiency virus (HIV) --- oral cell --- arachidonic acid cascade --- Kampo medicine --- lignin-carbohydrate complex --- traditional medicine --- eugenol --- QSAR analysis --- constituent plant extract --- polyphenol --- benzaldehyde --- glucosyltransferase --- infective endocarditis --- antiviral --- periodontitis --- nutritionally variant streptococci --- Kampo --- quantitative structure-activity relationship (QSAR) analysis --- traditional Japanese herbal medicine --- technical terms --- allergic rhinitis --- nasal epithelial cell --- antimicrobial susceptibilities --- alkaline extract --- mastic --- stomatitis --- thioredoxin --- production --- oral microbiota --- Jixueteng --- oral inflammation --- random forest --- mice --- chromone --- natural products --- Chinese herbal remedies --- inflammation --- quercetin --- in vivo --- kampo formula --- glucocorticoids --- Hangeshashinto --- recurrent aphthous stomatitis --- anti-osteoclast activity --- cytotoxicity --- dental application --- tongue diagnosis --- natural product --- alkaloids --- inflammatory disease --- pathogenic factors --- increase --- machine learning --- human virus --- cepharanthin --- mucositis --- oral diseases --- Juzentaihoto --- in vitro --- herbal medicine --- tumour-specificity

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This book is a printed edition of the Special Issue Molecular Modeling in Drug Design that was published in Molecules

metadynamics --- natural compounds --- virtual screening --- probe energies --- molecular dynamics simulation --- human ecto-5?-nucleotidase --- neural networks --- quantitative structure-activity relationship (QSAR) --- artificial intelligence --- allosterism --- in silico screening --- drug discovery --- amyloid fibrils --- mechanical stability --- adenosine receptors --- adenosine receptor --- ligand binding --- promiscuous mechanism --- AutoGrid --- dynamic light scattering --- resultant dipole moment --- density-based clustering --- Alzheimer’s disease --- drug design --- biophenols --- enzymatic assays --- all-atom molecular dynamics simulation --- fragment screening --- adenosine --- docking --- molecular docking --- cosolvent molecular dynamics --- turbidimetry --- squalene synthase (SQS) --- molecular recognition --- protein-peptide interactions --- extracellular loops --- FimH --- binding affinity --- rational drug design --- de novo design --- hyperlipidemia --- AR ligands --- aggregation --- property prediction --- PPI inhibition --- deep learning --- proteins --- quantitative structure-property prediction (QSPR) --- protein protein interactions --- boron cluster --- target-focused pharmacophore modeling --- ligand–protofiber interactions --- structure-based drug design --- scoring function --- grid maps --- solvent effect --- adhesion --- molecular dynamics --- Traditional Chinese Medicine --- steered molecular dynamics --- interaction energy --- EphA2-ephrin A1 --- molecular modeling --- method development