Do Optibrium’s predictive models work?
Data curation for model building A model can only be as good as the data it has been trained on.…
The power of AI applied to agrochemical bioactivity
In the face of growing agrochemical resistance and increasingly stringent regulatory requirements, how can artificial intelligence (AI) be harnessed to help lower the costs, failure rates and timelines associated with current agrochemical development cycles?
Perfecting the use of imperfect QSAR models
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.
AI Solutions from hit to candidate
In this webinar, we explore the highlights of collaborative project results that demonstrate how every phase of the drug discovery process can be radically improved by applying proven AI technology. Providing scientists with insights on which to base decisions can identify valuable new opportunities and reduce the time and cost of AI drug discovery cycles.
We review case studies from collaborations with Constellation Pharmaceuticals, AstraZeneca, Genentech, the University of Dundee and Takeda Pharmaceuticals to validate the impact of applying AI to experimental data and illustrate dramatic improvements to their project outcomes.
Join Samar Mahmoud and Matt Segall for this fascinating deep dive into the revolution that AI is bringing to the challenges of sparse and noisy drug discovery data.
Prediction of in vivo pharmacokinetic parameters and time – exposure curves in rats using machine learning from the chemical structure
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.
Predicting pharmacokinetic parameters and curves
Pharmacokinetics (PK) describes how the body affects a drug after administration. The concentration-time profile of a compound reflects its exposure,…
ADMET property prediction: The state of the art and current challenges
Summary This article discusses Quantitative Structure – Activity relationships (QSAR) methods to predict absorption, distribution, metabolism, excretion and toxicity (ADMET)…
