Quantitative Structure - Activity Relationship: A Practical Approach

Hamzeh-mivehroud, Maryam/ Sokouti, Babak

ISBN 10: 1032095458 / ISBN 13: 9781032095455

Editore: CRC Pr I Llc, 2021

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114 pages. 9.21x6.14x0.35 inches. In Stock.

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Dati bibliografici

• Titolo: Quantitative Structure - Activity Relationship: A Practical Approach
• Editore: CRC Pr I Llc
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1032095458
• ISBN 13: 9781032095455
• Rilegatura: Paperback
• Condizione: Brand New
• Peso: 0,23 chilogrammi

Descrizione articolo

Riassunto

Generally speaking, quantitative-structure activity relationship (QSAR) is a technique which correlates the biological activities of a set of compounds to their structures using a mathematical equation represented in its general form by Biological Activity = f (x1, ..., xn), where f is a mathematical function and x1, ..., xn are n molecular descriptors. Since the introduction of the initial concept of QSAR in the early 1960s, numerous advances have been introduced into the field transforming it into an essential tool in drug discovery and medicinal chemistry.

Quantitative Structure – Activity Relationship: A Practical Approach provides a detailed overview of computational approaches in QSAR studies. It covers the applications of different algorithms in various steps of a QSAR analysis and shows clear examples. Each chapter introduces the tools and software involved. Moreover, challenges and issues which may be faced in any step of the analysis are thoroughly broken down based on the OECD guidelines, enabling the reader to familiarize themselves with potential end results.

The book was kept concise, making it suitable for students (pharmacy, chemistry and biological science) and lecturers, as well as researchers in the field.

Informazioni sull?autore

Professor Siavoush Dastmalchi - graduated as Doctor of Pharmacy from Tabriz University of Medical Sciences (TUMS). Then he moved to Sydney where he received his PhD from the Faculty of Pharmacy at the University of Sydney in 2002. Since then he has worked as a full academic in the Medicinal Chemistry Department at the School of Pharmacy, TUMS, teaching medicinal chemistry, instrumental drug analysis and bioinformatics to graduate and postgraduate students. He is currently the Director of the Biotechnology Research Centre and Head of Medicinal Chemistry Department at TUMS where he leads his research team mainly with interests in molecular modeling, structural biology, and chemo-bioinformatics for their application to drug discovery.

Dr. Maryam Hamzeh-Mivehroud - Maryam Hamzeh-Mivehroud is the Associate Professor of Medicinal Chemistry who graduated as Doctor of Pharmacy from Tabriz University of Medical Sciences in 2004 and received her PhD from the same university in 2011. Since then she has worked as a full academic member in the Medicinal Chemistry Department at the School of Pharmacy, and teaches medicinal chemistry at the undergraduate and postgraduate levels. Her main research interests are focused on QSAR and molecular modeling in the field of drug design and discovery.

Dr. Babak Sokouti - Babak Sokouti is Assistant Professor of Bioinformatics in Biotechnology Research Center at Tabriz University of Medical Sciences with over 19 years IT technical management and consulting experience, including managing and maintaining sophisticated network infrastructures. He has obtained Bachelor of Science in Electrical Engineering with a specialization in Control from Isfahan University of Technology, Isfahan, Iran; a Master of Science in Electrical Engineering with a specialization in Electronics (with background of biomedical engineering) from Tabriz Branch, Islamic Azad University, Tabriz, Iran; a Master of Science in Information Security with Distinction from Royal Holloway University of London, London, UK; and obtained PhD in Bioinformatics from Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. His research interests include cryptographic algorithms, information security, network security and protocols, image processing, protein structure prediction, and hybrid intelligent neural network systems based on genetic algorithms.