Pharmacokinetics (PK) describes how the body affects a drug after administration. The concentration-time profile of a compound reflects its exposure, duration of action, safety margin and other critical factors affecting the success of a potential drug.
Accurate predictions of PK would enable better decisions regarding the selection of compounds for in vivo studies, reducing the number of experiments required and the associated cost. But, this is particularly challenging because in vivo PK is influenced by many biological mechanisms.
In this webinar, Tom Whitehead from Intellegens, describes the successful application of the Alchemite™ method for deep learning imputation to the prediction of PK parameters, based on compound structure and sparse in vitro data.
This project was undertaken in collaboration with AstraZeneca and we were delighted to be joined by Nigel Greene, AZ’s Director of Data Science & AI, who discussed their research in this area.
Peer-reviewed study published in Xenobiotica describes an innovative new method that predicts the routes and products of Phase I and II metabolism with high sensitivity and greater precision than
other approaches
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
Learn more about how AI, machine learning and other computational tools can support the discovery process, bringing you feasible synthetic routes to your target compounds.
