How to Judge Predictive Quality of Classification and Regression Based QSAR Models?
Pp. 71-120 (50)
Kunal Roy and Supratik Kar
Quantitative structure-activity relationship (QSAR) is a statistical modelling
approach that can be used in drug discovery, environmental fate modeling, property and
activity prediction of new, untested compounds. Validation has been identified as one of
the important steps for checking the robustness and reliability of QSAR models. Various
methodological aspects of validation of QSARs have been a subject of strong debate within
the academic and regulatory communities. One of the principles (Principle 4) of the
Organization for Economic Cooperation and Development (OECD) refers to the need to
establish “appropriate measures of goodness-of-fit, robustness and predictivity” for any
QSAR model. Validation strategies are recognized decisive steps to check the statistical
acceptability and applicability of the constructed models on a new set of data in order to
judge the confidence of predictions. Validation is a holistic practice that comprises
evaluation of issues such as quality of data, applicability of the model for prediction
purpose and mechanistic interpretation in addition to statistical judgment. Validation
strategies are largely dependent on various validation metrics. Viewing the importance of
QSAR validation approaches and different validation parameters in the development of
successful and acceptable QSAR models, we herein focus to have an overview of different
traditional as well as relatively new validation metrics used to judge the quality of the
regression as well as classification based QSAR models.
Applicability domain, OECD, QSAR, randomization, validation,
Manchester Institute of Biotechnology, University of Manchester Manchester M1 7DN, Great Britain.