The ability to predict the propensity of a molecule to lose or gain a proton in water is crucial for the development of new chemical entities with desirable pharmacokinetic (PK), absorption, distribution, metabolism and excretion (ADME) and binding properties. This study aimed to create a model using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).The resultant model displayed excellent accuracy, comparable to other much more computationally intensive methods.
P. Hunt, L. Hosseini-Gerami, T. Chrien, J. Plante, D. J. Ponting, M. Segall, J.Chem. Inf. Model., 2020, 60(6) pp2989-2997. DOI: 10.1021/acs.jcim.0c00105
Watch our webinar for an overview on how to predict pKa in StarDrop.
This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles.
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
This article describes the underlying methods, validation and example applications of the most recent models of Cytochrome P450 metabolism in…
