ABSTRACT
Validation Criteria of QSAR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .214 7.3 Validation of QSAR Models: Y-Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .220 7.4 Validation of QSAR Models: Training and Test Set Resampling. Stability
of QSAR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .220 7.5 Applicability Domains of QSAR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .222 7.6 Consensus Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .225 7.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .227 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .229
In this chapter, we continue to discuss the general framework of quantitative structure-activity relationships (QSAR) modeling. In the previous chapter, we have addressed the issue of data preparation for QSAR studies. Themain topic of this chapter is the general principles of QSAR model development and validation irrespective of specifics of any particular QSAR modeling routine. We introduce the concept of combinatorial QSAR modeling, which consists of building QSAR models for all combinations of descriptor types and optimization procedures. We classify QSAR approaches based on the response variable, which can be continuous (i.e., take multiple values spread over a certain interval), represent a category or rank of activity or property (e.g., very active, active, moderately active, and inactive), or a class of compounds (e.g., ligands of different receptors). For each type of the response variable, we introduce target functions that should be optimized by a QSAR procedure and criteria of model accuracy. Particular attention is paid to imbalanced datasets, in which the
counts of compounds belonging to different categories or classes are significantly different.We consider different validation procedures including cross-validation (which is included in model training), prediction for test sets (i.e., compounds that were not used inmodel training), andY-randomization test (i.e., building and evaluatingmodels with randomized activities of the response variable).We introduce a concept of model stability that can be established by resampling of training and test sets.We discuss the advantages and disadvantages of different definitions of applicability domains (AD) of QSARmodels. Finally, consensus prediction of external evaluation sets by all predictive models is considered as a test of the applicability of QSARmodels to chemical database mining and virtual screening. We emphasize that the integration of all the steps of QSARmodeling considered in both the previous chapter and this chapter in a unified workflow is critical for the development of validated and externally predictive QSAR models.
