Robust Theoretical Models in Medicinal Chemistry
QSAR, Artificial Intelligence, Machine Learning, and Deep Learning
Elsevier (Publisher)
Will be published approx. on 1. December 2026
Book
Paperback/Softback
350 pages
978-0-443-27420-6 (ISBN)
Description
Robust Theoretical Models in Medicinal Chemistry: QSAR, Artificial Intelligence, Machine Learning, and Deep Learning serves as a valuable resource chock full of applications extending into multiple knowledge domains. The meticulous construction of a robust model holds significance, not only in drug discovery but also in engineering, chemistry, pharmaceutical, and food-related research, illustrating the broad spectrum of fields where QSAR methodologies can be instrumental. The activities considered in QSAR span chemical measurements and biological assays, making this approach a versatile tool applicable across various scientific domains. Currently, QSAR finds extensive use in diverse disciplines, prominently in drug design and environmental risk assessment.
Quantitative Structure-Activity Relationships (QSAR) represent a concerted effort to establish correlations between structural or property descriptors of compounds and their respective activities. These physicochemical descriptors encompass a wide array of parameters, accounting for hydrophobicity, topology, electronic properties, and steric effects, and can be determined empirically or, more recently, through advanced computational methods.
Quantitative Structure-Activity Relationships (QSAR) represent a concerted effort to establish correlations between structural or property descriptors of compounds and their respective activities. These physicochemical descriptors encompass a wide array of parameters, accounting for hydrophobicity, topology, electronic properties, and steric effects, and can be determined empirically or, more recently, through advanced computational methods.
More details
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 152 mm
Weight
450 gr
ISBN-13
978-0-443-27420-6 (9780443274206)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Luciana Scotti is currently working as Senior Researcher at the Federal University of Paraiba, Brazil, where she completed her Ph.D. in Pharmacy. Her main area of interest focuses on Molecular and Computer Sciences. Her area of expertise includes Bioinformatics, Neglected Disease, Molecular Modelling, QSAR, and Natural Products. She has published 35 research articles in journals as well as 1 book and 2 book chapters contributed as author/co-author. Marcus Tullius Scotti studied chemical engineering at Universidade de Sao Paulo (USP - Sao Paulo University) and finished his degree in 1999. After, he worked for four years in a Brazilian electronics and telecommunications services company called Gradiente. At the same time, he started to study specialization on Industrial Administration at the University of Sao Paulo. After that, he started post-graduation in organic chemistry at the University of Sao Paulo in 2003 and finished his Master in 2005 and Ph.D. in 2008. In January of 2009, he moved to Joao Pessoa and started to work as Professor of Organic Chemistry at Universidade Federal da Paraiba (Federal University of Paraiba), Brazil. At beginning of 2014 he finished a post-doc in cheminformatics at Universidade Nova de Lisboa, Portugal. His research interests are in the chemistry of the natural products, acting on the following subjects: QSAR, Virtual Screening, molecular descriptors and chemotaxonomy using cheminformatics methods using statistical tools and machine learning algorithms. He has published mor than 240 papers, 19 book chapters and 155 abstracts in conferences.
Editor
Senior Researcher, Federal University of Paraiba, Brazil
Federal University of Paraiba, Paraiba, Brazil
Content
1. Building QSAR models
2. Model, validation and prediction
3. Outliers and Negative Data
4. QSAR3- and 4D
5. QSAR and QSRP modelling
6. QSAR In Food Science
7. Interpretation of recent computational methods
8. Recent theoretical methods in the industry
9. Understanding the difference between machine learning and deep learning
10. Can artificial intelligence replace QSAR?
2. Model, validation and prediction
3. Outliers and Negative Data
4. QSAR3- and 4D
5. QSAR and QSRP modelling
6. QSAR In Food Science
7. Interpretation of recent computational methods
8. Recent theoretical methods in the industry
9. Understanding the difference between machine learning and deep learning
10. Can artificial intelligence replace QSAR?