Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Predicting regioselectivity of AO, CYP, FMO and UGT metabolism using quantum mechanical simulations and machine learning
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
AI in early drug discovery: from promise to practice
In this webinar, Jeff Blaney (Senior Director of Discovery Chemistry, Genentech), Darren Green (Head of Cheminformatics & Data Science, GlaxoSmithKline), Julian Levell (Head of Discovery, New Equilibrium Biosciences), Matthew Segall (CEO, Optibrium) discuss the state of AI in early drug discovery from hit to preclinical candidate and share their experiences with and expectations of AI, including predictive modelling, synthesis prediction, and generative chemistry. Hear about the successes of AI drug discovery and an outlook on what AI needs to achieve to really transform the industry.
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.
Experimental validation of predictive models in a series of novel antimalarials
In this study, we identified a new antimalarial with an unusual structure – the only compound in the competition to be proven active, opening up new chemistry for exploration.
Imputation of sensory properties using deep learning
In this article, the team demonstrates the application of Alchemite™, a deep learning imputation method which underpins our Cerella™ technology, to physicochemical and sensory data.
Optibrium releases 3D ligand-based design module for StarDrop drug discovery software
Innovative predictive methods support virtual screening and compound design in the absence of 3D structure data.
Data visualisation: saying it all in a bite-sized chunk
In this webinar, we look at how we can use data visualisation in an impactful and effective way to communicate many dimensions of information. We illustrate some of the ways that we can achieve this and discuss visual methods to guide our decisions in drug discovery.
Deep imputation on large-scale drug discovery data
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
Multi-parameter optimisation in practice
This webinar describes example applications of multi-parameter optimisation to find high-quality lead compounds.
Practical applications of deep learning to impute heterogeneous drug discovery data
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
Predicting pKa using a combination of quantum mechanical and machine learning methods
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
A novel scoring profile for the design of antibacterials active against gram-negative bacteria
Introduction The increasing occurrence of multidrug-resistant bacteria is one of the major global threats to human health. Design of new…
Practical applications of matched series analysis
This paper, co-authored with our colleagues at NextMove Software, explores applications of Matched Series Analysis within StarDrop’s Nova module to…
Matched Series for SAR Analysis
Try Matched Series Analysis in this follow-along tutorial
The challenges of making decisions using uncertain data
This peer-reviewed paper discusses the challenges of using uncertain experimental data to make confident decisions on the selection of compounds.…
Breaking free from chemical spreadsheets
This article explores the benefits of a more intuitive and flexible approach to viewing and interacting with drug discovery data,…
Advances in multi-parameter optimisation methods for de novo drug design
Summary This review article discusses recent developments in the methods and opinions around multi-parameter optimisation, focusing on applications to de novo drug…
Preprint: considering the impact of ‘drug-like’ properties on the chance of success
Summary There are many different definitions of ‘drug-like’, with rules proposed based on property trends seen in successful drugs. In…