Robust Theoretical Models in Medicinal Chemistry
- Nyhet
QSAR, Artificial Intelligence, Machine Learning, and Deep Learning
Häftad, Engelska, 2026
2 669 kr
Kommande
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.
- Provides specific introductions and discussions on QSAR theory and methods
- Analyzes QSAR applicability in Pharmaceutical Chemistry, Food Science, and Environmental Sciences
- Builds, validates, and interprets robust, predictive, and reliable QSAR models
Produktinformation
- Utgivningsdatum2026-09-01
- Mått152 x 229 x undefined mm
- FormatHäftad
- SpråkEngelska
- Antal sidor350
- FörlagElsevier Science
- ISBN9780443274206