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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Robust Theoretical Models in Medicinal Chemistry: Qsar, Artificial Intelligence, Machine Learning, and Deep Learning

Robust Theoretical Models in Medicinal Chemistry: Qsar, Artificial Intelligence, Machine Learning, and Deep Learning

Scotti, Luciana
Scotti, Marcus Tullius

Paperback

Medical ReferenceChemistry

PREORDER - Expected ship date May 1, 2027

ISBN10: 0443274207
ISBN13: 9780443274206
Publisher: Elsevier
Published: May 1 2027
Pages: 350
Language: English
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.

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