QSAR modeling using automatically updating correction libraries: application to a human plasma protein binding model

Abstract

It is assumed that compounds occupying the same region of model space will be subject to similar errors in prediction, and hence, where these errors are known, they can be applied to predictions. Thus, any available measured data can be used to refine predictions of query compounds. This study describes the application of a correction library to a human plasma protein binding model. Compounds that have been measured since the model was built are entered into the library to improve predictions of current compounds. Time-series simulations were conducted to measure the time dependence of the correction library. This study demonstrates significant improvements in predictions where a library is applied, compared with both a static model and an updating model that includes recently measured data.