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After a necessary introduction to replace the QSAR in its historical context and methodology, we try to evaluate its potential, limitations and the ways that are utilized nowadays. For this purpose, the European evaluation program of chemical safety (REACH) is used as a concrete example of opportunity for QSAR. The advantages and disadvantages associated to the QSAR use within REACH, will be judged both economically and ethically but foremost from the point of view of toxicology. Following this, we will try to explain and to objectify the poor results of QSAR that are often criticized and the highlight the recent efforts mad to correct them. The prediction accuracy and certainly of the chemical activity that use a simple equation stay a myth. But the utility of QSAR for some purposed is itself real. Après une brève introduction visant à situer le QSAR dans son contexte historique et méthodologique, nous tenterons d’évaluer son potentiel, ses limites et les manières dont les modèles sont actuellement mis à profit. Pour ce faire, le programme d’évaluation de la sureté des substances chimiques au sein de l’Union Européenne (REACH) est utilisé comme exemple concret et actuel de débouchés pour le QSAR. Les avantages et inconvénients étant associés à son utilisation au sein de REACH, seront jugés tant au point de vue économique qu’éthique et avant tout du point de vue toxicologique. Suite à cela, nous tenterons d’expliquer et d’objectiver le manque de résultats souvent reproché au QSAR et soulignerons les récents efforts réalisés pour y remédier. Si prédire avec exactitude et certitude l’activité d’une substance chimique à l’aide d’une simple équation reste confiné au domaine du mythe, l’utilité du QSAR à certaines fins est quant à elle bien réelle.
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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.
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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.
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Drug Design --- Pharmaceutical Preparations --- Computational Biology --- Computer Simulation --- Models, Molecular --- Quantitative Structure-Activity Relationship --- Software --- Pharmaceutical chemistry --- Chemicals --- Chemicals --- Chemical reactions --- Drug development --- Structure-activity relationships (Biochemistry) --- Biomolecules
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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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QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment. Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment.
Drugs --- QSAR (Biochemistry) --- Quantitative structure-activity relationships (Biochemistry) --- Structure-activity relationships (Biochemistry) --- Structure-activity relationship (Pharmacology) --- Pharmaceutical chemistry --- Pharmacology --- Structure-activity relationships. --- Quantitative Structure-Activity Relationship --- Consumer Product Safety --- Environmental Pollutants --- Toxicity Tests
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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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Chemistry --- QSAR (Biochemistry) --- Structure-activity relationships (Biochemistry) --- Drugs --- Quantitative Structure-Activity Relationship. --- Combinatorial Chemistry Techniques. --- Biology. --- Pharmacology. --- Relations structure-activité quantitatives (Biochimie) --- Relations structure-activité (Biochimie) --- Médicaments --- Structure-activity relationships --- Relations structure-activité --- Structure-activity relationships. --- Health Sciences --- Life Sciences --- Biochemistry --- Pharmacy and Pharmacology --- Biochemorphology --- Biomolecules --- Chemical structure-biological activity relationships --- Relationships, Structure-activity (Biochemistry) --- Quantitative structure-activity relationships (Biochemistry) --- Structure-activity relationship (Pharmacology) --- Pharmacologies --- Pharmaceutical Preparations --- Chemistry Technic, Combinatorial --- Chemistry Technics, Combinatorial --- Chemistry Technique, Combinatorial --- Combinatorial Chemistry Technic --- Combinatorial Chemistry Technics --- Combinatorial Chemistry Technique --- Technic, Combinatorial Chemistry --- Technics, Combinatorial Chemistry --- Technique, Combinatorial Chemistry --- Chemistry Techniques, Combinatorial --- Techniques, Combinatorial Chemistry --- 3D-QSAR --- QSAR --- QSPR Modeling --- Quantitative Structure Property Relationship --- Structure Activity Relationship, Quantitative --- 3D QSAR --- 3D-QSARs --- Modeling, QSPR --- Quantitative Structure Activity Relationship --- Quantitative Structure-Activity Relationships --- Relationship, Quantitative Structure-Activity --- Relationships, Quantitative Structure-Activity --- Structure-Activity Relationship, Quantitative --- Structure-Activity Relationships, Quantitative --- Medicaments --- Medications --- Medicine (Drugs) --- Medicines (Drugs) --- Pharmaceuticals --- Prescription drugs --- pharmacology --- Physical biochemistry --- Pharmaceutical chemistry --- Pharmacology --- Gene Library --- Peptide Library --- Oligonucleotide Array Sequence Analysis --- High-Throughput Screening Assays --- Click Chemistry --- Bioactive compounds --- Medical supplies --- Pharmacopoeias --- Chemotherapy --- Materia medica --- Pharmacy --- Pharmacy, Therapeutics, & Pharmacology
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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
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