This peer-reviewed article, published in the Journal of Medicinal Chemistry, describes how identifying sensitive criteria can highlight new avenues for exploration, and assist us in avoiding missed opportunities
This article describes the underlying methods, validation and example applications of the most recent models of Cytochrome P450 metabolism in…
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
This peer-reviewed paper discusses the challenges of using uncertain experimental data to make confident decisions on the selection of compounds.…
We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands.
Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk.
This article explores the benefits of a more intuitive and flexible approach to viewing and interacting with drug discovery data,…
Summary In this drug optimisation article, co-authored with Pfizer we discuss new ‘rule induction’ methods. These explore complex data to…
We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships.
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.
This peer-reviewed paper in Xenobiotica describes a new method to determine the most likely experimentally-observed routes of metabolism and metabolites based on our WhichP450™, regioselectivity and new WhichEnzyme™ model.
Systematic optimisation of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead optimisation using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate.
The solution structure of the minor conformer of rapamycin was investigated using a combination of NMR techniques and computational methods
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity.
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
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.
We present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction.
