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.
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
Learn how advances in informatics technology are inspiring a new generation of innovative products that streamline and enhance the efficiency and productivity of drug discovery software.
Methods for modelling two enzyme families, flavin-containing monoxygenases (FMOs) and uridine 5′-diphospho-glucuronosyltransferases (UGTs), to predict reactivity to drug metabolism.
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
In this webinar, we explore how the limitations of pharmaceutical data can impact conventional predictive model building. Our speakers, Julian Levell (Constellation pharmaceuticals), Ben Irwin and matt Segall (Optibrium) demonstrate how the deep learning imputation algorithm underlying our Cerella platform, overcomes these challenges.
Introduction Existing computational models of drug metabolism are heavily focused on predicting oxidation by cytochrome P450 (CYP) enzymes, because of…
Introduction The increasing occurrence of multidrug-resistant bacteria is one of the major global threats to human health. Design of new…
This paper describes the underlying methods and validation of the WhichP450 model, which predicts the most likely Cytochrome P450 isoforms…
We introduce the ForceGen method for 3D structure generation and conformer elaboration of drug-like small molecules.
This article describes the underlying methods, validation and example applications of the most recent models of Cytochrome P450 metabolism in…
Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk.
Summary In this drug optimisation article, co-authored with Pfizer we discuss new ‘rule induction’ methods. These explore complex data to…
Summary In this article, ‘Addressing toxicity risk when designing and selecting compounds in early drug discovery‘, we discuss the application…
This paper demonstrates how the Surflex-PSIM method can help investigate hypotheses around protein function in cases where function cannot be determined by sequence similarity.
In the article, Bickerton et al. (2012) “The Chemical Beauty of Drugs” Nature Chemistry 4, 90–98, the authors proposed a measure of ‘drug-likeness’, the Quantitative…
Summary There are many different definitions of ‘drug-like’, with rules proposed based on property trends seen in successful drugs. In…
Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.”
This article discusses logic fallacies in the context of off-target predictive modelling.