Bøger af Kunal Roy

This second edition volume expands on the previous edition with updated descriptions on different computational methods encompassing ligand-based, structure-based, and combined approaches with their recent applications in anti-Alzheimer drug design. Different background topics like recent advancements in research on the development of novel therapies and their implications in the treatment of Alzheimer's Disease (AD) have also been covered for completeness. Special topics like basic information science methods for insight into neurodegenerative pathogenesis, drug repositioning and network pharmacology, and online tools to predict ADMET behavior with reference to anti-Alzheimer drug development have also been included. In the Neuromethods series style, chapter include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.Cutting-edge and thorough, Computational Modeling of Drugs Against Alzheimer's Disease, Second Edition is a valuable resource for all researchers and scientists interested in learning more about this important and developing field.

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.

This essential volume explores a variety of tools and protocols of structure-based (homology modeling, molecular docking, molecular dynamics, protein-protein interaction network) and ligand-based (pharmacophore mapping, quantitative structure-activity relationships or QSARs) drug design for ranking and prioritization of candidate molecules in search of effective treatment strategy against coronaviruses. Beginning with an introductory section that discusses coronavirus interactions with humanity and COVID-19 in particular, the book then continues with sections on tools and methodologies, literature reports and case studies, as well as online tools and databases that can be used for computational anti-coronavirus drug research. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical detail and implementation advice that ensures high quality results in the lab. Comprehensive and timely, In Silico Modeling ofDrugs Against Coronaviruses: Computational Tools and Protocols is an ideal reference for researchers working on the development of novel anti-coronavirus drugs for SARS-CoV-2 and for coronaviruses that will likely appear in the future.

Das Buch Chemometrics and Cheminformatics in Aquatic Toxicology befasst sich mit den bestehenden und neu auftretenden Problemen der Verschmutzung der aquatischen Umwelt durch verschiedene metallische und organische Schadstoffe, insbesondere Industriechemikalien, Pharmazeutika, Kosmetika, Biozide, Nanomaterialien, Pestizide, Tenside, Farbstoffe und viele weitere. Es werden verschiedene chemometrische und cheminformatische Instrumente für Laien beschrieben mitsamt ihrer Anwendung auf die Analyse und Modellierung der Toxizitätsdaten von Chemikalien in Bezug auf unterschiedliche aquatische Organismen.Eine Reihe von Datenbanken zur aquatischen Toxizität sowie chemometrische Softwaretools und Webserver werden vorgestellt und praktische Beispiele für die Modellentwicklung gegeben, einschließlich der entsprechenden Abbildungen. Darüber hinaus enthält das Werk Fallstudien und Literaturberichte, um das Verständnis des Themas abzurunden. Außerdem lernen die Leserinnen und Leser Werkzeuge und Protokolle wie maschinelles Lernen, Data Mining sowie Methoden des QSAR-basierten und ligandenbasierten chemischen Designs kennen.Darüber hinaus bietet das Werk:* Eine umfassende Einführung in chemometrische und cheminformatische Instrumente und Techniken, insbesondere maschinelles Lernen und Data Mining* Eine Darstellung von Datenbanken zur aquatischen Toxizität, chemometrischen Softwaretools und Webservern* Praktische Beispiele und Fallstudien zur Verdeutlichung und Veranschaulichung der im Buch enthaltenen Konzepte* Eine kompakte Erläuterung der chemometrischen und cheminformatischen Instrumente sowie ihrer Anwendung auf die Analyse und Modellierung von ToxizitätsdatenChemometrics and Cheminformatics in Aquatic Toxicology ist ideal für Forschende und Studierende der Chemie sowie der Umwelt- und Pharmawissenschaften und sollte auch in den Bibliotheken von Fachleuten in der chemischen Industrie sowie Aufsichtsbehörden, die sich mit Chemometrie beschäftigen, einen Platz finden.