{"product_id":"9780443274206","title":"Robust Theoretical Models in Medicinal Chemistry : QSAR, Artificial Intelligence, Machine Learning, and Deep Learning by Luciana Scotti","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.\u003cbr\u003eBinding: Paperback \/ softback","brand":"Gardners","offers":[{"title":"Default Title","offer_id":56367766634869,"sku":"9780443274206","price":162.99,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/backstory.london\/products\/9780443274206","provider":"Backstory","version":"1.0","type":"link"}