Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

ISBN 10: 0443186383 / ISBN 13: 9780443186387

Editore: Academic Press, 2023

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Codice articolo 18396069854

Dati bibliografici

• Titolo: Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development
• Editore: Academic Press
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 0443186383
• ISBN 13: 9780443186387
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Dr. Kunal Roy is a Professor and Ex-Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013). The field of his research interest is QSAR and Molecular Modeling with application in Drug Design and Ecotoxicological Modeling. Dr. Roy has published more than 350 research articles in refereed journals (current SCOPUS h index 49). He has also coauthored two QSAR-related books, edited six QSAR books and published more than ten book chapters. Dr. Roy is a Co-Editor-in-Chief of Molecular Diversity (Springer Nature). He also serves as a member of the Editorial Boards of several International Journals.