Building Predictive QSAR Models The Importance ofValidation [Pg.438]

Golbraikh, a. Tropsha, a. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. [Pg.455]

Application of Predictive QSAR Models to Database Mining [Pg.437]

Tropsha, A., Golbraikh, A. (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr PharmaceutDesign 13, 3494-3504. [Pg.50]

A growing list of predictive QSAR models derived from BioPrint data e.g. solubility, log D (pH = 7.4, pH = 6.5), Cyp2D6 inhibition, permeability (A B, B A, passive), and more than 30 target-related models. [Pg.192]

Nendza, M. (1991) Predictive QSAR models estimating ecotoxic hazard of phenylureas Aquatic toxicity. Chemosphere 23, 497-506. [Pg.824]

Tropsha, a. Application of predictive QSAR models to database mining. [Pg.197]

Degner, P., Nendza, M., and Klein, W., Predictive QSAR models for estimating biodegradation of aromatic compounds, Sci. Total Environ., 109, 253-259, 1991. [Pg.390]

J. P., Kohn, H., Tropsha, A. (2004) Application of predictive QSAR models to database mining identification and experimental validation of novel anticonvulsant compounds./ Med Chem 47, 2356-2364. [Pg.131]

Our experience in QSAR model development and validation has led us to establishing a complex strategy that is summarized in Fig. 6.2. It describes the predictive QSAR modeling work-flow, which focuses on delivering validated models and ultimately, computational hits confirmed by the experimental validation. We [Pg.116]

There is a long history of efforts to find simple and interpretable /i and fi functions for various activities and properties (29, 30). The quest for predictive QSAR models started with Hammett s pioneer work to correlate molecular structures with chemical reactivities (30-32). However, the widespread applications of modern predictive QSAR and QSPR actually started with the seminal work of Hansch and coworkers on pesticides (29, 33, 34) and the developments of various powerful analysis tools, such as PLS (partial least squares) and neural networks, for multivariate analysis have fueled these widespread applications. Nowadays, numerous publications on guidelines, workflows, and [Pg.40]

The present paper is organized as follows. First, a brief introduction is given for each ADME-Tox property, especially how it is related to ADME-Tox, and then the latest in silico models for that property are discussed. Further, some experts opinion is presented on how to model that property more accurately and reliably. This is followed by a discussion on how to build up predictable QSAR models with all kinds of statistical tools. Finally, the ADME-Tox resources, including both databases and software packages are summarized. [Pg.104]

Fraction of the variance The fraction of the variance of an MRA model is expressed by r. It is beheved that the closer the value of to unity, the better the QSAR model. The values of for these QSAR models are from 0.787 to 0.993, which suggests that these QSAR models explain 78.7-99.3% of the variance of the data. According to the literature, the predictive QSAR model must have > 0.6 [73,74]. [Pg.69]

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