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- W43690112 abstract "This chapter focuses on modern and developing trends in quantitative structure–activity relationship (QSAR) modeling. This research methodology is based on two strongly interrelated components, i.e., molecular ‘descriptors’ and statistical ‘data modeling’ techniques that are used to correlate descriptors with the ‘target property’ such as biological activity. The QSAR modeling field has experienced significant changes over its more than 40 years in existence. Its complexity has increased both with respect to molecular descriptors and data modeling techniques with the relatively recent emphasis of the latter methods on machine learning approaches. Some of these novel methods are reviewed in the chapter briefly but the real emphasis is placed on ‘model validation’ that has not been given sufficient attention in the past. We discuss general internal and external validation approaches that can be used by QSAR practitioners irrespective of the specific modeling approach and propose general QSAR modeling workflow. We emphasize that only extensively validated models characterized by their ‘applicability domains’ could be considered reliable and predictive. In the last section of the chapter, we demonstrate that validated, predictive models could be used successfully as a means of virtual screening of chemical libraries for the effective discovery of novel biologically active compounds." @default.
- W43690112 created "2016-06-24" @default.
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- W43690112 date "2007-01-01" @default.
- W43690112 modified "2023-10-18" @default.
- W43690112 title "Predictive Quantitative Structure–Activity Relationship Modeling" @default.
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- W43690112 doi "https://doi.org/10.1016/b0-08-045044-x/00248-0" @default.
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