Gedeck, Peter, Lu, Yipin, Skolnik, Suzanne, Rodde, Stephane, Dollinger, Gavin, Jia, Weiping, Berellini, Giuliano, Vianello, Riccardo, Faller, Bernard and Lombardo, Franco (2016) The benefit of retraining pKa models studied using internally measured data. Journal of Chemical Information and Modeling.

Abstract

The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation constant pKa during the lead optimization process to make better-informed design decisions. Computational approaches, as implemented in MoKa, can help with this, however often predict with a too large error. In this contribution, we look at how retraining helps to greatly improve prediction error. Using a longitudinal study with data measured over 15 years in a drug discovery environment, we assess the impact of model training on prediction accuracy and look at model degradation over time. Using the MoKa software, we will demonstrate that regular retraining is required to address changes in chemical space leading to model degradation over six to nine months.

Item Type:	Article
Keywords:	QSAR pKa training simulation modelling
Date Deposited:	08 Mar 2017 00:45
Last Modified:	08 Mar 2017 00:45
URI:	https://oak.novartis.com/id/eprint/24568