QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality.

ABSTRACT: We propose that quantitative structure-activity relationship (QSAR) predictions should be explicitly represented as predictive (probability) distributions. If both predictions and experimental measurements are treated as probability distributions, the quality of a set of predictive distributions output by a model can be assessed with Kullback-Leibler (KL) divergence: a widely used information theoretic measure of the distance between two probability distributions. We have assessed a range of different machine learning algorithms and error estimation methods for producing predictive distributions with an analysis against three of AstraZeneca's global DMPK datasets. Using the KL-divergence framework, we have identified a few combinations of algorithms that produce accurate and valid compound

SUBMITTER: Wood DJ 

PROVIDER: S-EPMC3639359 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature